def learn(policy, env, seed, nsteps=5, nstack=4, total_timesteps=int(80e6), vf_coef=0.5, ent_coef=0.01, max_grad_norm=0.5, lr=7e-4, lrschedule='linear', epsilon=1e-5, alpha=0.99, gamma=0.99, log_interval=100):
tf.reset_default_graph()
set_global_seeds(seed)
nenvs = env.num_envs
ob_space = env.observation_space
ac_space = env.action_space
num_procs = len(env.remotes) # HACK
model = Model(policy=policy, ob_space=ob_space, ac_space=ac_space, nenvs=nenvs, nsteps=nsteps, nstack=nstack, num_procs=num_procs, ent_coef=ent_coef, vf_coef=vf_coef,
max_grad_norm=max_grad_norm, lr=lr, alpha=alpha, epsilon=epsilon, total_timesteps=total_timesteps, lrschedule=lrschedule)
runner = Runner(env, model, nsteps=nsteps, nstack=nstack, gamma=gamma)
nbatch = nenvs*nsteps
tstart = time.time()
for update in range(1, total_timesteps//nbatch+1):
obs, states, rewards, masks, actions, values = runner.run()
policy_loss, value_loss, policy_entropy = model.train(obs, states, rewards, masks, actions, values)
nseconds = time.time()-tstart
fps = int((update*nbatch)/nseconds)
if update % log_interval == 0 or update == 1:
ev = explained_variance(values, rewards)
logger.record_tabular("nupdates", update)
logger.record_tabular("total_timesteps", update*nbatch)
logger.record_tabular("fps", fps)
logger.record_tabular("policy_entropy", float(policy_entropy))
logger.record_tabular("value_loss", float(value_loss))
logger.record_tabular("explained_variance", float(ev))
logger.dump_tabular()
env.close()
def learn(policy, env, seed, total_timesteps=int(40e6), gamma=0.99, log_interval=1, nprocs=32, nsteps=20,
ent_coef=0.01, vf_coef=0.5, vf_fisher_coef=1.0, lr=0.25, max_grad_norm=0.5,
kfac_clip=0.001, save_interval=None, lrschedule='linear'):
tf.reset_default_graph()
set_global_seeds(seed)
nenvs = env.num_envs
ob_space = env.observation_space
ac_space = env.action_space
make_model = lambda : Model(policy, ob_space, ac_space, nenvs, total_timesteps, nprocs=nprocs, nsteps
=nsteps, ent_coef=ent_coef, vf_coef=vf_coef, vf_fisher_coef=
vf_fisher_coef, lr=lr, max_grad_norm=max_grad_norm, kfac_clip=kfac_clip,
lrschedule=lrschedule)
if save_interval and logger.get_dir():
import cloudpickle
with open(osp.join(logger.get_dir(), 'make_model.pkl'), 'wb') as fh:
fh.write(cloudpickle.dumps(make_model))
model = make_model()
runner = Runner(env, model, nsteps=nsteps, gamma=gamma)
nbatch = nenvs*nsteps
tstart = time.time()
coord = tf.train.Coordinator()
enqueue_threads = model.q_runner.create_threads(model.sess, coord=coord, start=True)
for update in range(1, total_timesteps//nbatch+1):
obs, states, rewards, masks, actions, values = runner.run()
policy_loss, value_loss, policy_entropy = model.train(obs, states, rewards, masks, actions, values)
model.old_obs = obs
nseconds = time.time()-tstart
fps = int((update*nbatch)/nseconds)
if update % log_interval == 0 or update == 1:
ev = explained_variance(values, rewards)
logger.record_tabular("nupdates", update)
logger.record_tabular("total_timesteps", update*nbatch)
logger.record_tabular("fps", fps)
logger.record_tabular("policy_entropy", float(policy_entropy))
logger.record_tabular("policy_loss", float(policy_loss))
logger.record_tabular("value_loss", float(value_loss))
logger.record_tabular("explained_variance", float(ev))
logger.dump_tabular()
if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir():
savepath = osp.join(logger.get_dir(), 'checkpoint%.5i'%update)
print('Saving to', savepath)
model.save(savepath)
coord.request_stop()
coord.join(enqueue_threads)
env.close()
def learn(policy,
env,
seed,
total_timesteps=int(40e6),
gamma=0.99,
log_interval=1,
nprocs=24,
nscripts=12,
nsteps=20,
nstack=4,
ent_coef=0.01,
vf_coef=0.5,
vf_fisher_coef=1.0,
lr=0.25,
max_grad_norm=0.01,
kfac_clip=0.001,
save_interval=None,
lrschedule='linear',
callback=None):
tf.reset_default_graph()
set_global_seeds(seed)
nenvs = nprocs
ob_space = (32, 32, 3) # env.observation_space
ac_space = (32, 32)
make_model = lambda: Model(policy, ob_space, ac_space, nenvs,
total_timesteps,
nprocs=nprocs,
nscripts=nscripts,
nsteps=nsteps,
nstack=nstack,
ent_coef=ent_coef,
vf_coef=vf_coef,
vf_fisher_coef=vf_fisher_coef,
lr=lr,
max_grad_norm=max_grad_norm,
kfac_clip=kfac_clip,
lrschedule=lrschedule)
if save_interval and logger.get_dir():
import cloudpickle
with open(osp.join(logger.get_dir(), 'make_model.pkl'), 'wb') as fh:
fh.write(cloudpickle.dumps(make_model))
model = make_model()
print("make_model complete!")
runner = Runner(
env,
model,
nsteps=nsteps,
nscripts=nscripts,
nstack=nstack,
gamma=gamma,
callback=callback)
nbatch = nenvs * nsteps
tstart = time.time()
# enqueue_threads = model.q_runner.create_threads(model.sess, coord=tf.train.Coordinator(), start=True)
for update in range(1, total_timesteps // nbatch + 1):
obs, states, td_targets, masks, actions, xy0, xy1, values = runner.run()
policy_loss, value_loss, policy_entropy, \
policy_loss_xy0, policy_entropy_xy0, \
policy_loss_xy1, policy_entropy_xy1, \
= model.train(obs, states, td_targets,
masks, actions,
xy0, xy1, values)
model.old_obs = obs
nseconds = time.time() - tstart
fps = int((update * nbatch) / nseconds)
if update % log_interval == 0 or update == 1:
ev = explained_variance(values, td_targets)
# nsml.report(
# nupdates=update,
# total_timesteps=update * nbatch,
# fps=fps,
# policy_entropy=float(policy_entropy),
# policy_loss=float(policy_loss),
# policy_loss_xy0=float(policy_loss_xy0),
# policy_entropy_xy0=float(policy_entropy_xy0),
# policy_loss_xy1=float(policy_loss_xy1),
# policy_entropy_xy1=float(policy_entropy_xy1),
# value_loss=float(value_loss),
# explained_variance=float(ev),
# batch_size=nbatch,
# step=update,
# scope=locals()
# )
# logger.record_tabular("nupdates", update)
# logger.record_tabular("total_timesteps", update * nbatch)
# logger.record_tabular("fps", fps)
# logger.record_tabular("policy_entropy", float(policy_entropy))
# logger.record_tabular("policy_loss", float(policy_loss))
# logger.record_tabular("policy_loss_xy0", float(policy_loss_xy0))
# logger.record_tabular("policy_entropy_xy0",
# float(policy_entropy_xy0))
# logger.record_tabular("policy_loss_xy1", float(policy_loss_xy1))
# logger.record_tabular("policy_entropy_xy1",
# float(policy_entropy_xy1))
# # logger.record_tabular("policy_loss_y0", float(policy_loss_y0))
# # logger.record_tabular("policy_entropy_y0", float(policy_entropy_y0))
# logger.record_tabular("value_loss", float(value_loss))
# logger.record_tabular("explained_variance", float(ev))
# logger.dump_tabular()
if save_interval and (update % save_interval == 0
or update == 1) and logger.get_dir():
savepath = osp.join(logger.get_dir(), 'checkpoint%.5i' % update)
print('Saving to', savepath)
model.save(savepath)
env.close()
def learn(
*,
network,
env,
total_timesteps,
timesteps_per_batch,
sil_update,
sil_loss, # what to train on
max_kl=0.001,
cg_iters=10,
gamma=0.99,
lam=1.0, # advantage estimation
seed=None,
ent_coef=0.0,
lr=3e-4,
cg_damping=1e-2,
vf_stepsize=3e-4,
vf_iters=5,
sil_value=0.01,
sil_alpha=0.6,
sil_beta=0.1,
max_episodes=0,
max_iters=0, # time constraint
callback=None,
save_interval=0,
load_path=None,
model_fn=None,
update_fn=None,
init_fn=None,
mpi_rank_weight=1,
comm=None,
vf_coef=0.5,
max_grad_norm=0.5,
log_interval=1,
nminibatches=4,
noptepochs=4,
cliprange=0.2,
**network_kwargs):
#last_perfm=[]
'''
learn a policy function with TRPO algorithm
Parameters:
----------
network neural network to learn. Can be either string ('mlp', 'cnn', 'lstm', 'lnlstm' for basic types)
or function that takes input placeholder and returns tuple (output, None) for feedforward nets
or (output, (state_placeholder, state_output, mask_placeholder)) for recurrent nets
env environment (one of the gym environments or wrapped via baselines.common.vec_env.VecEnv-type class
timesteps_per_batch timesteps per gradient estimation batch
max_kl max KL divergence between old policy and new policy ( KL(pi_old || pi) )
ent_coef coefficient of policy entropy term in the optimization objective
cg_iters number of iterations of conjugate gradient algorithm
cg_damping conjugate gradient damping
vf_stepsize learning rate for adam optimizer used to optimie value function loss
vf_iters number of iterations of value function optimization iterations per each policy optimization step
total_timesteps max number of timesteps
max_episodes max number of episodes
max_iters maximum number of policy optimization iterations
callback function to be called with (locals(), globals()) each policy optimization step
load_path str, path to load the model from (default: None, i.e. no model is loaded)
**network_kwargs keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
Returns:
-------
learnt model
'''
set_global_seeds(seed)
if isinstance(lr, float): lr = constfn(lr)
else: assert callable(lr)
if isinstance(cliprange, float): cliprange = constfn(cliprange)
else: assert callable(cliprange)
if MPI is not None:
nworkers = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
else:
nworkers = 1
rank = 0
cpus_per_worker = 1
U.get_session(
config=tf.ConfigProto(allow_soft_placement=True,
inter_op_parallelism_threads=cpus_per_worker,
intra_op_parallelism_threads=cpus_per_worker))
policy = build_policy(env, network, value_network='copy', **network_kwargs)
nenvs = env.num_envs
np.set_printoptions(precision=3)
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
nbatch = nenvs * timesteps_per_batch
nbatch_train = nbatch // nminibatches
is_mpi_root = (MPI is None or MPI.COMM_WORLD.Get_rank() == 0)
ob = observation_placeholder(ob_space)
with tf.variable_scope("pi", reuse=tf.AUTO_REUSE):
pi = policy(observ_placeholder=ob)
#sil_model=policy(None, None, sess=get_session)
make_model = lambda: Model(
policy=policy,
ob_space=ob_space,
ac_space=ac_space,
nbatch_act=nenvs,
nbatch_train=nbatch_train,
nsteps=timesteps_per_batch,
ent_coef=ent_coef,
vf_coef=vf_coef,
max_grad_norm=max_grad_norm,
sil_update=sil_update,
sil_value=sil_value,
sil_alpha=sil_alpha,
sil_beta=sil_beta,
sil_loss=sil_loss,
# fn_reward=env.process_reward,
fn_reward=None,
# fn_obs=env.process_obs,
fn_obs=None,
ppo=False,
prev_pi='pi',
silm=pi)
model = make_model()
if load_path is not None:
model.load(load_path)
with tf.variable_scope("oldpi", reuse=tf.AUTO_REUSE):
oldpi = policy(observ_placeholder=ob)
make_old_model = lambda: Model(
policy=policy,
ob_space=ob_space,
ac_space=ac_space,
nbatch_act=nenvs,
nbatch_train=nbatch_train,
nsteps=timesteps_per_batch,
ent_coef=ent_coef,
vf_coef=vf_coef,
max_grad_norm=max_grad_norm,
sil_update=sil_update,
sil_value=sil_value,
sil_alpha=sil_alpha,
sil_beta=sil_beta,
sil_loss=sil_loss,
# fn_reward=env.process_reward,
fn_reward=None,
# fn_obs=env.process_obs,
fn_obs=None,
ppo=False,
prev_pi='oldpi',
silm=oldpi)
old_model = make_old_model()
atarg = tf.placeholder(
dtype=tf.float32,
shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
ac = pi.pdtype.sample_placeholder([None])
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
entbonus = ent_coef * meanent
vferr = tf.reduce_mean(tf.square(pi.vf - ret))
ratio = tf.exp(pi.pd.logp(ac) -
oldpi.pd.logp(ac)) # advantage * pnew / pold
surrgain = tf.reduce_mean(ratio * atarg)
optimgain = surrgain + entbonus
losses = [optimgain, meankl, entbonus, surrgain, meanent]
loss_names = ["optimgain", "meankl", "entloss", "surrgain", "Entropy"]
dist = meankl
all_var_list = get_trainable_variables("pi")
# var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("pol")]
# vf_var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("vf")]
var_list = get_pi_trainable_variables("pi")
vf_var_list = get_vf_trainable_variables("pi")
vfadam = MpiAdam(vf_var_list)
get_flat = U.GetFlat(var_list)
set_from_flat = U.SetFromFlat(var_list)
klgrads = tf.gradients(dist, var_list)
flat_tangent = tf.placeholder(dtype=tf.float32,
shape=[None],
name="flat_tan")
shapes = [var.get_shape().as_list() for var in var_list]
start = 0
tangents = []
for shape in shapes:
sz = U.intprod(shape)
tangents.append(tf.reshape(flat_tangent[start:start + sz], shape))
start += sz
gvp = tf.add_n([
tf.reduce_sum(g * tangent)
for (g, tangent) in zipsame(klgrads, tangents)
]) #pylint: disable=E1111
fvp = U.flatgrad(gvp, var_list)
assign_old_eq_new = U.function(
[], [],
updates=[
tf.assign(oldv, newv)
for (oldv,
newv) in zipsame(get_variables("oldpi"), get_variables("pi"))
])
compute_losses = U.function([ob, ac, atarg], losses)
compute_lossandgrad = U.function([ob, ac, atarg], losses +
[U.flatgrad(optimgain, var_list)])
compute_fvp = U.function([flat_tangent, ob, ac, atarg], fvp)
compute_vflossandgrad = U.function([ob, ret],
U.flatgrad(vferr, vf_var_list))
@contextmanager
def timed(msg):
if rank == 0:
print(colorize(msg, color='magenta'))
tstart = time.time()
yield
print(
colorize("done in %.3f seconds" % (time.time() - tstart),
color='magenta'))
else:
yield
def allmean(x):
assert isinstance(x, np.ndarray)
if MPI is not None:
out = np.empty_like(x)
MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
out /= nworkers
else:
out = np.copy(x)
return out
U.initialize()
if load_path is not None:
pi.load(load_path)
th_init = get_flat()
if MPI is not None:
MPI.COMM_WORLD.Bcast(th_init, root=0)
set_from_flat(th_init)
vfadam.sync()
print("Init param sum", th_init.sum(), flush=True)
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(env,
timesteps_per_batch,
model,
stochastic=True)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=40) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=40) # rolling buffer for episode rewards
if sum([max_iters > 0, total_timesteps > 0, max_episodes > 0]) == 0:
# noththing to be done
return pi
assert sum([max_iters>0, total_timesteps>0, max_episodes>0]) < 2, \
'out of max_iters, total_timesteps, and max_episodes only one should be specified'
while True:
if callback: callback(locals(), globals())
if total_timesteps and timesteps_so_far >= total_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
logger.log("********** Iteration %i ************" % iters_so_far)
with timed("sampling"):
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, gamma, lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg[
"tdlamret"]
vpredbefore = seg["vpred"] # predicted value function before udpate
model = seg["model"]
atarg = (atarg - atarg.mean()
) / atarg.std() # standardized advantage function estimate
if hasattr(pi, "ret_rms"): pi.ret_rms.update(tdlamret)
if hasattr(pi, "rms"):
pi.rms.update(ob) # update running mean/std for policy
args = seg["ob"], seg["ac"], atarg
fvpargs = [arr[::5] for arr in args]
def fisher_vector_product(p):
return allmean(compute_fvp(p, *fvpargs)) + cg_damping * p
assign_old_eq_new() # set old parameter values to new parameter values
with timed("computegrad"):
*lossbefore, g = compute_lossandgrad(*args)
lossbefore = allmean(np.array(lossbefore))
g = allmean(g)
if np.allclose(g, 0):
logger.log("Got zero gradient. not updating")
else:
with timed("cg"):
stepdir = cg(fisher_vector_product,
g,
cg_iters=cg_iters,
verbose=rank == 0)
assert np.isfinite(stepdir).all()
shs = .5 * stepdir.dot(fisher_vector_product(stepdir))
lm = np.sqrt(shs / max_kl)
# logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g))
fullstep = stepdir / lm
expectedimprove = g.dot(fullstep)
surrbefore = lossbefore[0]
stepsize = 1.0
thbefore = get_flat()
for _ in range(10):
thnew = thbefore + fullstep * stepsize
set_from_flat(thnew)
meanlosses = surr, kl, *_ = allmean(
np.array(compute_losses(*args)))
improve = surr - surrbefore
logger.log("Expected: %.3f Actual: %.3f" %
(expectedimprove, improve))
if not np.isfinite(meanlosses).all():
logger.log("Got non-finite value of losses -- bad!")
elif kl > max_kl * 1.5:
logger.log("violated KL constraint. shrinking step.")
elif improve < 0:
logger.log("surrogate didn't improve. shrinking step.")
else:
logger.log("Stepsize OK!")
break
stepsize *= .5
else:
logger.log("couldn't compute a good step")
set_from_flat(thbefore)
if nworkers > 1 and iters_so_far % 20 == 0:
paramsums = MPI.COMM_WORLD.allgather(
(thnew.sum(), vfadam.getflat().sum())) # list of tuples
assert all(
np.allclose(ps, paramsums[0]) for ps in paramsums[1:])
for (lossname, lossval) in zip(loss_names, meanlosses):
logger.record_tabular(lossname, lossval)
with timed("vf"):
for _ in range(vf_iters):
for (mbob, mbret) in dataset.iterbatches(
(seg["ob"], seg["tdlamret"]),
include_final_partial_batch=False,
batch_size=64):
g = allmean(compute_vflossandgrad(mbob, mbret))
vfadam.update(g, vf_stepsize)
with timed('SIL'):
lrnow = lr(1.0 - timesteps_so_far / total_timesteps)
l_loss, sil_adv, sil_samples, sil_nlogp = model.sil_train(lrnow)
logger.record_tabular("ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values
if MPI is not None:
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
else:
listoflrpairs = [lrlocal]
lens, rews = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("AverageReturn", np.mean(rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
#if ((total_timesteps % timesteps_per_batch==0) and (total_timesteps//timesteps_per_batch-iters_so_far<50)) or ((total_timesteps % timesteps_per_batch!=0) and (total_timesteps // timesteps_per_batch +1 - iters_so_far < 50)):
# last_perfm.append(np.mean(rewbuffer))
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
if sil_update > 0:
logger.record_tabular("SilSamples", sil_samples)
if rank == 0:
logger.dump_tabular()
return pi
def learn(
env,
policy_func,
*,
timesteps_per_batch,
max_kl,
cg_iters,
gamma,
lam,
entcoeff=0.0,
cg_damping=1e-2,
vf_stepsize=3e-4,
vf_iters=3,
max_timesteps=0,
max_episodes=0,
max_iters=0,
callback=None,
# GAIL Params
pretrained_weight=None,
reward_giver=None,
expert_dataset=None,
rank=0,
save_per_iter=1,
ckpt_dir="/tmp/gail/ckpt/",
g_step=1,
d_step=1,
task_name="task_name",
d_stepsize=3e-4,
using_gail=True):
"""
learns a GAIL policy using the given environment
:param env: (Gym Environment) the environment
:param policy_func: (function (str, Gym Space, Gym Space, bool): MLPPolicy) policy generator
:param timesteps_per_batch: (int) the number of timesteps to run per batch (horizon)
:param max_kl: (float) the kullback leiber loss threashold
:param cg_iters: (int) the number of iterations for the conjugate gradient calculation
:param gamma: (float) the discount value
:param lam: (float) GAE factor
:param entcoeff: (float) the weight for the entropy loss
:param cg_damping: (float) the compute gradient dampening factor
:param vf_stepsize: (float) the value function stepsize
:param vf_iters: (int) the value function's number iterations for learning
:param max_timesteps: (int) the maximum number of timesteps before halting
:param max_episodes: (int) the maximum number of episodes before halting
:param max_iters: (int) the maximum number of training iterations before halting
:param callback: (function (dict, dict)) the call back function, takes the local and global attribute dictionary
:param pretrained_weight: (str) the save location for the pretrained weights
:param reward_giver: (TransitionClassifier) the reward predicter from obsevation and action
:param expert_dataset: (MujocoDset) the dataset manager
:param rank: (int) the rank of the mpi thread
:param save_per_iter: (int) the number of iterations before saving
:param ckpt_dir: (str) the location for saving checkpoints
:param g_step: (int) number of steps to train policy in each epoch
:param d_step: (int) number of steps to train discriminator in each epoch
:param task_name: (str) the name of the task (can be None)
:param d_stepsize: (float) the reward giver stepsize
:param using_gail: (bool) using the GAIL model
"""
nworkers = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
np.set_printoptions(precision=3)
sess = tf_util.single_threaded_session()
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
policy = policy_func("pi", ob_space, ac_space, sess=sess)
old_policy = policy_func("oldpi",
ob_space,
ac_space,
sess=sess,
placeholders={
"obs": policy.obs_ph,
"stochastic": policy.stochastic_ph
})
atarg = tf.placeholder(
dtype=tf.float32,
shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
observation = policy.obs_ph
action = policy.pdtype.sample_placeholder([None])
kloldnew = old_policy.proba_distribution.kl(policy.proba_distribution)
ent = policy.proba_distribution.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
entbonus = entcoeff * meanent
vferr = tf.reduce_mean(tf.square(policy.vpred - ret))
# advantage * pnew / pold
ratio = tf.exp(
policy.proba_distribution.logp(action) -
old_policy.proba_distribution.logp(action))
surrgain = tf.reduce_mean(ratio * atarg)
optimgain = surrgain + entbonus
losses = [optimgain, meankl, entbonus, surrgain, meanent]
loss_names = ["optimgain", "meankl", "entloss", "surrgain", "entropy"]
dist = meankl
all_var_list = policy.get_trainable_variables()
if using_gail:
var_list = [
v for v in all_var_list
if v.name.startswith("pi/pol") or v.name.startswith("pi/logstd")
]
vf_var_list = [v for v in all_var_list if v.name.startswith("pi/vff")]
assert len(var_list) == len(vf_var_list) + 1
d_adam = MpiAdam(reward_giver.get_trainable_variables())
else:
var_list = [
v for v in all_var_list if v.name.split("/")[1].startswith("pol")
]
vf_var_list = [
v for v in all_var_list if v.name.split("/")[1].startswith("vf")
]
vfadam = MpiAdam(vf_var_list, sess=sess)
get_flat = tf_util.GetFlat(var_list, sess=sess)
set_from_flat = tf_util.SetFromFlat(var_list, sess=sess)
klgrads = tf.gradients(dist, var_list)
flat_tangent = tf.placeholder(dtype=tf.float32,
shape=[None],
name="flat_tan")
shapes = [var.get_shape().as_list() for var in var_list]
start = 0
tangents = []
for shape in shapes:
var_size = tf_util.intprod(shape)
tangents.append(tf.reshape(flat_tangent[start:start + var_size],
shape))
start += var_size
gvp = tf.add_n([
tf.reduce_sum(grad * tangent)
for (grad, tangent) in zipsame(klgrads, tangents)
]) # pylint: disable=E1111
fvp = tf_util.flatgrad(gvp, var_list)
assign_old_eq_new = tf_util.function(
[], [],
updates=[
tf.assign(oldv, newv) for (oldv, newv) in zipsame(
old_policy.get_variables(), policy.get_variables())
])
compute_losses = tf_util.function([observation, action, atarg], losses)
compute_lossandgrad = tf_util.function(
[observation, action, atarg],
losses + [tf_util.flatgrad(optimgain, var_list)])
compute_fvp = tf_util.function([flat_tangent, observation, action, atarg],
fvp)
compute_vflossandgrad = tf_util.function([observation, ret],
tf_util.flatgrad(
vferr, vf_var_list))
@contextmanager
def timed(msg):
if rank == 0:
print(colorize(msg, color='magenta'))
start_time = time.time()
yield
print(
colorize("done in %.3f seconds" % (time.time() - start_time),
color='magenta'))
else:
yield
def allmean(arr):
assert isinstance(arr, np.ndarray)
out = np.empty_like(arr)
MPI.COMM_WORLD.Allreduce(arr, out, op=MPI.SUM)
out /= nworkers
return out
tf_util.initialize(sess=sess)
th_init = get_flat()
MPI.COMM_WORLD.Bcast(th_init, root=0)
set_from_flat(th_init)
if using_gail:
d_adam.sync()
vfadam.sync()
if rank == 0:
print("Init param sum", th_init.sum(), flush=True)
# Prepare for rollouts
# ----------------------------------------
if using_gail:
seg_gen = traj_segment_generator(policy,
env,
timesteps_per_batch,
stochastic=True,
reward_giver=reward_giver,
gail=True)
else:
seg_gen = traj_segment_generator(policy,
env,
timesteps_per_batch,
stochastic=True)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
t_start = time.time()
lenbuffer = deque(maxlen=40) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=40) # rolling buffer for episode rewards
assert sum([max_iters > 0, max_timesteps > 0, max_episodes > 0]) == 1
if using_gail:
true_rewbuffer = deque(maxlen=40)
# Stats not used for now
# g_loss_stats = Stats(loss_names)
# d_loss_stats = Stats(reward_giver.loss_name)
# ep_stats = Stats(["True_rewards", "Rewards", "Episode_length"])
# if provide pretrained weight
if pretrained_weight is not None:
tf_util.load_state(pretrained_weight,
var_list=policy.get_variables())
while True:
if callback:
callback(locals(), globals())
if max_timesteps and timesteps_so_far >= max_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
# Save model
if using_gail and rank == 0 and iters_so_far % save_per_iter == 0 and ckpt_dir is not None:
fname = os.path.join(ckpt_dir, task_name)
os.makedirs(os.path.dirname(fname), exist_ok=True)
saver = tf.train.Saver()
saver.save(sess, fname)
logger.log("********** Iteration %i ************" % iters_so_far)
def fisher_vector_product(vec):
return allmean(compute_fvp(vec, *fvpargs,
sess=sess)) + cg_damping * vec
# ------------------ Update G ------------------
logger.log("Optimizing Policy...")
# g_step = 1 when not using GAIL
for _ in range(g_step):
with timed("sampling"):
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, gamma, lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
observation, action, atarg, tdlamret = seg["ob"], seg["ac"], seg[
"adv"], seg["tdlamret"]
vpredbefore = seg[
"vpred"] # predicted value function before udpate
atarg = (atarg - atarg.mean()) / atarg.std(
) # standardized advantage function estimate
if hasattr(policy, "ret_rms"):
policy.ret_rms.update(tdlamret)
if hasattr(policy, "ob_rms"):
policy.ob_rms.update(
observation) # update running mean/std for policy
args = seg["ob"], seg["ac"], atarg
fvpargs = [arr[::5] for arr in args]
assign_old_eq_new(sess=sess)
with timed("computegrad"):
*lossbefore, grad = compute_lossandgrad(*args, sess=sess)
lossbefore = allmean(np.array(lossbefore))
grad = allmean(grad)
if np.allclose(grad, 0):
logger.log("Got zero gradient. not updating")
else:
with timed("cg"):
stepdir = conjugate_gradient(fisher_vector_product,
grad,
cg_iters=cg_iters,
verbose=rank == 0)
assert np.isfinite(stepdir).all()
shs = .5 * stepdir.dot(fisher_vector_product(stepdir))
# abs(shs) to avoid taking square root of negative values
lagrange_multiplier = np.sqrt(abs(shs) / max_kl)
# logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g))
fullstep = stepdir / lagrange_multiplier
expectedimprove = grad.dot(fullstep)
surrbefore = lossbefore[0]
stepsize = 1.0
thbefore = get_flat()
for _ in range(10):
thnew = thbefore + fullstep * stepsize
set_from_flat(thnew)
mean_losses = surr, kl_loss, *_ = allmean(
np.array(compute_losses(*args, sess=sess)))
improve = surr - surrbefore
logger.log("Expected: %.3f Actual: %.3f" %
(expectedimprove, improve))
if not np.isfinite(mean_losses).all():
logger.log("Got non-finite value of losses -- bad!")
elif kl_loss > max_kl * 1.5:
logger.log("violated KL constraint. shrinking step.")
elif improve < 0:
logger.log("surrogate didn't improve. shrinking step.")
else:
logger.log("Stepsize OK!")
break
stepsize *= .5
else:
logger.log("couldn't compute a good step")
set_from_flat(thbefore)
if nworkers > 1 and iters_so_far % 20 == 0:
paramsums = MPI.COMM_WORLD.allgather(
(thnew.sum(),
vfadam.getflat().sum())) # list of tuples
assert all(
np.allclose(ps, paramsums[0]) for ps in paramsums[1:])
with timed("vf"):
for _ in range(vf_iters):
for (mbob, mbret) in dataset.iterbatches(
(seg["ob"], seg["tdlamret"]),
include_final_partial_batch=False,
batch_size=128):
if hasattr(policy, "ob_rms"):
policy.ob_rms.update(
mbob) # update running mean/std for policy
grad = allmean(
compute_vflossandgrad(mbob, mbret, sess=sess))
vfadam.update(grad, vf_stepsize)
for (loss_name, loss_val) in zip(loss_names, mean_losses):
logger.record_tabular(loss_name, loss_val)
logger.record_tabular("ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
if using_gail:
# ------------------ Update D ------------------
logger.log("Optimizing Discriminator...")
logger.log(fmt_row(13, reward_giver.loss_name))
ob_expert, ac_expert = expert_dataset.get_next_batch(
len(observation))
batch_size = len(observation) // d_step
d_losses = [
] # list of tuples, each of which gives the loss for a minibatch
for ob_batch, ac_batch in dataset.iterbatches(
(observation, action),
include_final_partial_batch=False,
batch_size=batch_size):
ob_expert, ac_expert = expert_dataset.get_next_batch(
len(ob_batch))
# update running mean/std for reward_giver
if hasattr(reward_giver, "obs_rms"):
reward_giver.obs_rms.update(
np.concatenate((ob_batch, ob_expert), 0))
*newlosses, grad = reward_giver.lossandgrad(
ob_batch, ac_batch, ob_expert, ac_expert)
d_adam.update(allmean(grad), d_stepsize)
d_losses.append(newlosses)
logger.log(fmt_row(13, np.mean(d_losses, axis=0)))
lrlocal = (seg["ep_lens"], seg["ep_rets"], seg["ep_true_rets"]
) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews, true_rets = map(flatten_lists, zip(*listoflrpairs))
true_rewbuffer.extend(true_rets)
else:
lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
if using_gail:
logger.record_tabular("EpTrueRewMean", np.mean(true_rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - t_start)
if rank == 0:
logger.dump_tabular()
def learn(
env,
policy_fn,
*,
timesteps_per_actorbatch, # timesteps per actor per update
clip_param,
entcoeff, # clipping parameter epsilon, entropy coeff
optim_epochs,
optim_stepsize,
optim_batchsize, # optimization hypers
gamma,
lam, # advantage estimation
max_timesteps=0,
max_episodes=0,
max_iters=0,
max_seconds=0, # time constraint
callback=None, # you can do anything in the callback, since it takes locals(), globals()
adam_epsilon=1e-5,
schedule='constant', # annealing for stepsize parameters (epsilon and adam)
gradients=True,
hessians=False,
model_path='model',
output_prefix,
sim):
#Directory setup:
model_dir = 'models/'
if not os.path.exists(model_dir):
os.makedirs(model_dir)
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_fn("pi", ob_space,
ac_space) # Construct network for new policy
oldpi = policy_fn("oldpi", ob_space, ac_space) # Network for old policy
atarg = tf.placeholder(
dtype=tf.float32,
shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
lrmult = tf.placeholder(
name='lrmult', dtype=tf.float32,
shape=[]) # learning rate multiplier, updated with schedule
clip_param = clip_param * lrmult # Annealed cliping parameter epislon
ob = U.get_placeholder_cached(name="ob")
ac = pi.pdtype.sample_placeholder([None])
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
pol_entpen = (-entcoeff) * meanent
ratio = tf.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac)) # pnew / pold
surr1 = ratio * atarg # surrogate from conservative policy iteration
surr2 = tf.clip_by_value(ratio, 1.0 - clip_param,
1.0 + clip_param) * atarg #
pol_surr = -tf.reduce_mean(tf.minimum(
surr1, surr2)) # PPO's pessimistic surrogate (L^CLIP)
vf_loss = tf.reduce_mean(tf.square(pi.vpred - ret))
total_loss = pol_surr + pol_entpen + vf_loss
losses = [pol_surr, pol_entpen, vf_loss, meankl, meanent]
loss_names = ["pol_surr", "pol_entpen", "vf_loss", "kl", "ent"]
var_list = pi.get_trainable_variables()
lossandgradandhessian = U.function(
[ob, ac, atarg, ret, lrmult], losses +
[U.flatgrad(total_loss, var_list),
U.flathess(total_loss, var_list)])
lossandgrad = U.function([ob, ac, atarg, ret, lrmult],
losses + [U.flatgrad(total_loss, var_list)])
adam = MpiAdam(var_list, epsilon=adam_epsilon)
assign_old_eq_new = U.function(
[], [],
updates=[
tf.assign(oldv, newv)
for (oldv,
newv) in zipsame(oldpi.get_variables(), pi.get_variables())
])
compute_losses = U.function([ob, ac, atarg, ret, lrmult], losses)
U.initialize()
# Set the logs writer to the folder /tmp/tensorflow_logs
tf.summary.FileWriter(
'/home/aespielberg/ResearchCode/baselines/baselines/tmp/',
graph_def=tf.get_default_session().graph_def)
adam.sync()
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi,
env,
timesteps_per_actorbatch,
stochastic=True)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=100) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=100) # rolling buffer for episode rewards
assert sum(
[max_iters > 0, max_timesteps > 0, max_episodes > 0,
max_seconds > 0]) == 1, "Only one time constraint permitted"
gradient_indices = get_gradient_indices(pi)
while True:
if callback: callback(locals(), globals())
#ANDYTODO: add new break condition
'''
try:
print(np.std(rewbuffer) / np.mean(rewbuffer))
print(rewbuffer)
if np.std(rewbuffer) / np.mean(rewbuffer) < 0.01: #TODO: input argument
break
except:
pass #No big
'''
if max_timesteps and timesteps_so_far >= max_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
elif max_seconds and time.time() - tstart >= max_seconds:
break
if schedule == 'constant':
cur_lrmult = 1.0
elif schedule == 'linear':
cur_lrmult = max(1.0 - float(timesteps_so_far) / max_timesteps, 0)
else:
raise NotImplementedError
logger.log("********** Iteration %i ************" % iters_so_far)
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, gamma, lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg[
"tdlamret"]
vpredbefore = seg["vpred"] # predicted value function before udpate
atarg = (atarg - atarg.mean()
) / atarg.std() # standardized advantage function estimate
d = Dataset(dict(ob=ob, ac=ac, atarg=atarg, vtarg=tdlamret),
shuffle=not pi.recurrent)
optim_batchsize = optim_batchsize or ob.shape[0]
if hasattr(pi, "ob_rms"):
pi.ob_rms.update(ob) # update running mean/std for policy
assign_old_eq_new() # set old parameter values to new parameter values
logger.log("Optimizing...")
logger.log(fmt_row(13, loss_names))
# Here we do a bunch of optimization epochs over the data
for _ in range(optim_epochs):
gradient_set = []
losses = [
] # list of tuples, each of which gives the loss for a minibatch
for batch in d.iterate_once(optim_batchsize):
*newlosses, g = lossandgrad(batch["ob"], batch["ac"],
batch["atarg"], batch["vtarg"],
cur_lrmult)
gradient_set.append(g)
if not sim:
adam.update(g, optim_stepsize * cur_lrmult)
losses.append(newlosses)
logger.log(fmt_row(13, np.mean(losses, axis=0)))
print('objective is')
print(np.sum(np.mean(losses, axis=0)[0:3]))
print(get_model_vars(pi))
if sim:
print('return routine')
return_routine(pi, d, batch, output_prefix, losses, cur_lrmult,
lossandgradandhessian, gradients, hessians,
gradient_set)
return pi
if np.mean(list(
map(np.linalg.norm,
gradient_set))) < 1e-4: #TODO: make this a variable
#TODO: abstract all this away somehow (scope)
print('minimized!')
return_routine(pi, d, batch, output_prefix, losses, cur_lrmult,
lossandgradandhessian, gradients, hessians,
gradient_set)
return pi
print(np.mean(list(map(np.linalg.norm, np.array(gradient_set)))))
logger.log("Evaluating losses...")
losses = []
for batch in d.iterate_once(optim_batchsize):
newlosses = compute_losses(batch["ob"], batch["ac"],
batch["atarg"], batch["vtarg"],
cur_lrmult)
losses.append(newlosses)
meanlosses, _, _ = mpi_moments(losses, axis=0)
logger.log(fmt_row(13, meanlosses))
for (lossval, name) in zipsame(meanlosses, loss_names):
logger.record_tabular("loss_" + name, lossval)
logger.record_tabular("ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
if MPI.COMM_WORLD.Get_rank() == 0:
logger.dump_tabular()
if iters_so_far > 1:
U.save_state(model_dir + model_path + str(iters_so_far))
print('out of time')
return_routine(pi, d, batch, output_prefix, losses, cur_lrmult,
lossandgradandhessian, gradients, hessians, gradient_set)
return pi
def learn(env,
policy_func,
reward_giver,
expert_dataset,
rank,
pretrained,
pretrained_weight,
*,
g_step,
d_step,
entcoeff,
save_per_iter,
ckpt_dir,
log_dir,
timesteps_per_batch,
task_name,
gamma,
lam,
max_kl,
cg_iters,
cg_damping=1e-2,
vf_stepsize=3e-4,
d_stepsize=3e-4,
vf_iters=3,
max_timesteps=0,
max_episodes=0,
max_iters=0,
callback=None,
writer=None):
nworkers = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
np.set_printoptions(precision=3)
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_func("pi",
ob_space,
ac_space,
reuse=(pretrained_weight != None))
oldpi = policy_func("oldpi", ob_space, ac_space)
atarg = tf.placeholder(
dtype=tf.float32,
shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
ob = U.get_placeholder_cached(name="ob")
ac = pi.pdtype.sample_placeholder([None])
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
entbonus = entcoeff * meanent
vferr = tf.reduce_mean(tf.square(pi.vpred - ret))
ratio = tf.exp(pi.pd.logp(ac) -
oldpi.pd.logp(ac)) # advantage * pnew / pold
surrgain = tf.reduce_mean(ratio * atarg)
optimgain = surrgain + entbonus
losses = [optimgain, meankl, entbonus, surrgain, meanent]
loss_names = ["optimgain", "meankl", "entloss", "surrgain", "entropy"]
dist = meankl
all_var_list = pi.get_trainable_variables()
var_list = [
v for v in all_var_list
if v.name.startswith("pi/pol") or v.name.startswith("pi/logstd")
]
vf_var_list = [v for v in all_var_list if v.name.startswith("pi/vff")]
assert len(var_list) == len(vf_var_list) + 1
d_adam = MpiAdam(reward_giver.get_trainable_variables())
vfadam = MpiAdam(vf_var_list)
get_flat = U.GetFlat(var_list)
set_from_flat = U.SetFromFlat(var_list)
klgrads = tf.gradients(dist, var_list)
flat_tangent = tf.placeholder(dtype=tf.float32,
shape=[None],
name="flat_tan")
shapes = [var.get_shape().as_list() for var in var_list]
start = 0
tangents = []
for shape in shapes:
sz = U.intprod(shape)
tangents.append(tf.reshape(flat_tangent[start:start + sz], shape))
start += sz
gvp = tf.add_n([
tf.reduce_sum(g * tangent)
for (g, tangent) in zipsame(klgrads, tangents)
]) # pylint: disable=E1111
fvp = U.flatgrad(gvp, var_list)
assign_old_eq_new = U.function(
[], [],
updates=[
tf.assign(oldv, newv)
for (oldv,
newv) in zipsame(oldpi.get_variables(), pi.get_variables())
])
compute_losses = U.function([ob, ac, atarg], losses)
compute_lossandgrad = U.function([ob, ac, atarg], losses +
[U.flatgrad(optimgain, var_list)])
compute_fvp = U.function([flat_tangent, ob, ac, atarg], fvp)
compute_vflossandgrad = U.function([ob, ret],
U.flatgrad(vferr, vf_var_list))
@contextmanager
def timed(msg):
if rank == 0:
print(colorize(msg, color='magenta'))
tstart = time.time()
yield
print(
colorize("done in %.3f seconds" % (time.time() - tstart),
color='magenta'))
else:
yield
def allmean(x):
assert isinstance(x, np.ndarray)
out = np.empty_like(x)
MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
out /= nworkers
return out
U.initialize()
th_init = get_flat()
MPI.COMM_WORLD.Bcast(th_init, root=0)
set_from_flat(th_init)
d_adam.sync()
vfadam.sync()
if rank == 0:
print("Init param sum", th_init.sum(), flush=True)
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi,
env,
reward_giver,
timesteps_per_batch,
stochastic=True)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=40) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=40) # rolling buffer for episode rewards
true_rewbuffer = deque(maxlen=40)
assert sum([max_iters > 0, max_timesteps > 0, max_episodes > 0]) == 1
# g_loss_stats = stats(loss_names)
# d_loss_stats = stats(reward_giver.loss_name)
#ep_stats = stats(["True_rewards", "Rewards", "Episode_length"])
ep_stats = stats(["True_rewards", "Episode_length"])
# if provide pretrained weight
if pretrained_weight is not None:
U.load_state(pretrained_weight, var_list=pi.get_variables())
while True:
if callback: callback(locals(), globals())
if max_timesteps and timesteps_so_far >= max_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
# Save model
if rank == 0 and iters_so_far % save_per_iter == 0 and ckpt_dir is not None:
fname = os.path.join(ckpt_dir, task_name)
os.makedirs(os.path.dirname(fname), exist_ok=True)
saver = tf.train.Saver()
saver.save(tf.get_default_session(), fname)
logger.log("********** Iteration %i ************" % iters_so_far)
def fisher_vector_product(p):
return allmean(compute_fvp(p, *fvpargs)) + cg_damping * p
# ------------------ Update G ------------------
logger.log("Optimizing Policy...")
for _ in range(g_step):
with timed("sampling"):
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, gamma, lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg[
"tdlamret"]
vpredbefore = seg[
"vpred"] # predicted value function before udpate
atarg = (atarg - atarg.mean()) / atarg.std(
) # standardized advantage function estimate
if hasattr(pi, "ob_rms"):
pi.ob_rms.update(ob) # update running mean/std for policy
args = seg["ob"], seg["ac"], atarg
fvpargs = [arr[::5] for arr in args]
assign_old_eq_new(
) # set old parameter values to new parameter values
with timed("computegrad"):
*lossbefore, g = compute_lossandgrad(*args)
lossbefore = allmean(np.array(lossbefore))
g = allmean(g)
if np.allclose(g, 0):
logger.log("Got zero gradient. not updating")
else:
with timed("cg"):
stepdir = cg(fisher_vector_product,
g,
cg_iters=cg_iters,
verbose=rank == 0)
assert np.isfinite(stepdir).all()
shs = .5 * stepdir.dot(fisher_vector_product(stepdir))
lm = np.sqrt(shs / max_kl)
# logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g))
fullstep = stepdir / lm
expectedimprove = g.dot(fullstep)
surrbefore = lossbefore[0]
stepsize = 1.0
thbefore = get_flat()
for _ in range(10):
thnew = thbefore + fullstep * stepsize
set_from_flat(thnew)
meanlosses = surr, kl, *_ = allmean(
np.array(compute_losses(*args)))
improve = surr - surrbefore
logger.log("Expected: %.3f Actual: %.3f" %
(expectedimprove, improve))
if not np.isfinite(meanlosses).all():
logger.log("Got non-finite value of losses -- bad!")
elif kl > max_kl * 1.5:
logger.log("violated KL constraint. shrinking step.")
elif improve < 0:
logger.log("surrogate didn't improve. shrinking step.")
else:
logger.log("Stepsize OK!")
break
stepsize *= .5
else:
logger.log("couldn't compute a good step")
set_from_flat(thbefore)
if nworkers > 1 and iters_so_far % 20 == 0:
paramsums = MPI.COMM_WORLD.allgather(
(thnew.sum(),
vfadam.getflat().sum())) # list of tuples
assert all(
np.allclose(ps, paramsums[0]) for ps in paramsums[1:])
with timed("vf"):
for _ in range(vf_iters):
for (mbob, mbret) in dataset.iterbatches(
(seg["ob"], seg["tdlamret"]),
include_final_partial_batch=False,
batch_size=128):
if hasattr(pi, "ob_rms"):
pi.ob_rms.update(
mbob) # update running mean/std for policy
g = allmean(compute_vflossandgrad(mbob, mbret))
vfadam.update(g, vf_stepsize)
g_losses = meanlosses
for (lossname, lossval) in zip(loss_names, meanlosses):
logger.record_tabular(lossname, lossval)
logger.record_tabular("ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
# ------------------ Update D ------------------
logger.log("Optimizing Discriminator...")
logger.log(fmt_row(13, reward_giver.loss_name))
ob_expert, ac_expert = expert_dataset.get_next_batch(len(ob))
batch_size = len(ob) // d_step
d_losses = [
] # list of tuples, each of which gives the loss for a minibatch
for ob_batch, ac_batch in dataset.iterbatches(
(ob, ac), include_final_partial_batch=False,
batch_size=batch_size):
ob_expert, ac_expert = expert_dataset.get_next_batch(len(ob_batch))
# update running mean/std for reward_giver
if hasattr(reward_giver, "obs_rms"):
reward_giver.obs_rms.update(
np.concatenate((ob_batch, ob_expert), 0))
*newlosses, g = reward_giver.lossandgrad(ob_batch, ac_batch,
ob_expert, ac_expert)
d_adam.update(allmean(g), d_stepsize)
d_losses.append(newlosses)
logger.log(fmt_row(13, np.mean(d_losses, axis=0)))
lrlocal = (seg["ep_lens"], seg["ep_rets"], seg["ep_true_rets"]
) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews, true_rets = map(flatten_lists, zip(*listoflrpairs))
true_rewbuffer.extend(true_rets)
lenbuffer.extend(lens)
rewbuffer.extend(rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpTrueRewMean", np.mean(true_rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
if writer is not None:
ep_stats.add_all_summary(
writer, [np.mean(true_rewbuffer),
np.mean(lenbuffer)], episodes_so_far)
if rank == 0:
logger.dump_tabular()
def learn(*, policy, env, nsteps, total_timesteps, ent_coef, lr,
vf_coef=0.5, max_grad_norm=0.5, gamma=0.99, lam=0.95,
log_interval=10, nminibatches=4, noptepochs=4, cliprange=0.2,
save_interval=0, load_path=None, num_casks=0):
if isinstance(lr, float): lr = constfn(lr)
else: assert callable(lr)
if isinstance(cliprange, float): cliprange = constfn(cliprange)
else: assert callable(cliprange)
total_timesteps = int(total_timesteps)
nenvs = env.num_envs - num_casks
ob_space = env.observation_space
ac_space = env.action_space
nbatch = nenvs * nsteps
nbatch_train = nbatch // nminibatches
make_model = lambda : Model(policy=policy, ob_space=ob_space, ac_space=ac_space, nbatch_act=env.num_envs, nbatch_train=nbatch_train,
nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef,
max_grad_norm=max_grad_norm)
if save_interval and logger.get_dir():
import cloudpickle
with open(osp.join(logger.get_dir(), 'make_model.pkl'), 'wb') as fh:
fh.write(cloudpickle.dumps(make_model))
model = make_model()
if load_path is not None:
model.load(load_path)
# load running mean std
checkdir = load_path[0:-5]
checkpoint = int(load_path.split('/')[-1])
if osp.exists(osp.join(checkdir, '%.5i_ob_rms.pkl' % checkpoint)):
with open(osp.join(checkdir, '%.5i_ob_rms.pkl' % checkpoint), 'rb') as ob_rms_fp:
env.ob_rms = pickle.load(ob_rms_fp)
# if osp.exists(osp.join(checkdir, '%.5i_ret_rms.pkl' % checkpoint)):
# with open(osp.join(checkdir, '%.5i_ret_rms.pkl' % checkpoint), 'rb') as ret_rms_fp:
# env.ret_rms = pickle.load(ret_rms_fp)
# tensorboard
writer = tf.summary.FileWriter(logger.get_dir(), tf.get_default_session().graph)
runner = Runner(env=env, model=model, nsteps=nsteps, gamma=gamma, lam=lam, writer=writer, num_casks=num_casks)
epinfobuf = deque(maxlen=100)
tfirststart = time.time()
nupdates = total_timesteps//nbatch
for update in range(1, nupdates+1):
assert nbatch % nminibatches == 0
nbatch_train = nbatch // nminibatches
tstart = time.time()
frac = 1.0 - (update - 1.0) / nupdates
lrnow = lr(frac)
cliprangenow = cliprange(frac)
obs, returns, masks, actions, values, neglogpacs, states, epinfos = runner.run() #pylint: disable=E0632
epinfobuf.extend(epinfos)
mblossvals = []
if states is None: # nonrecurrent version
inds = np.arange(nbatch)
for _ in range(noptepochs):
np.random.shuffle(inds)
for start in range(0, nbatch, nbatch_train):
end = start + nbatch_train
mbinds = inds[start:end]
slices = (arr[mbinds] for arr in (obs, returns, masks, actions, values, neglogpacs))
mblossvals.append(model.train(lrnow, cliprangenow, *slices))
else: # recurrent version
assert nenvs % nminibatches == 0
envsperbatch = nenvs // nminibatches
envinds = np.arange(nenvs)
flatinds = np.arange(nenvs * nsteps).reshape(nenvs, nsteps)
envsperbatch = nbatch_train // nsteps
for _ in range(noptepochs):
np.random.shuffle(envinds)
for start in range(0, nenvs, envsperbatch):
end = start + envsperbatch
mbenvinds = envinds[start:end]
mbflatinds = flatinds[mbenvinds].ravel()
slices = (arr[mbflatinds] for arr in (obs, returns, masks, actions, values, neglogpacs))
mbstates = states[mbenvinds]
mblossvals.append(model.train(lrnow, cliprangenow, *slices, mbstates))
lossvals = np.mean(mblossvals, axis=0)
tnow = time.time()
fps = int(nbatch / (tnow - tstart))
if update % log_interval == 0 or update == 1:
ev = explained_variance(values, returns)
logger.logkv("serial_timesteps", update*nsteps)
logger.logkv("nupdates", update)
logger.logkv("total_timesteps", update*nbatch)
logger.logkv("fps", fps)
logger.logkv("explained_variance", float(ev))
logger.logkv('eprewmean', safemean([epinfo['r'] for epinfo in epinfobuf]))
logger.logkv('eplenmean', safemean([epinfo['l'] for epinfo in epinfobuf]))
logger.logkv('epsrewmean', safemean([epinfo['sr'] for epinfo in epinfobuf]))
logger.logkv('time_elapsed', tnow - tfirststart)
for (lossval, lossname) in zip(lossvals, model.loss_names):
logger.logkv(lossname, lossval)
logger.dumpkvs()
# tensorboard
summary = tf.Summary()
summary.value.add(tag='iteration/reward_mean', simple_value=safemean([epinfo['r'] for epinfo in epinfobuf]))
summary.value.add(tag='iteration/length_mean', simple_value=safemean([epinfo['l'] for epinfo in epinfobuf]))
summary.value.add(tag='iteration/shaped_reward_mean', simple_value=safemean([epinfo['sr'] for epinfo in epinfobuf]))
summary.value.add(tag='iteration/fps', simple_value=fps)
writer.add_summary(summary, update)
if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir():
checkdir = osp.join(logger.get_dir(), 'checkpoints')
os.makedirs(checkdir, exist_ok=True)
savepath = osp.join(checkdir, '%.5i'%update)
print('Saving to', savepath)
model.save(savepath)
# save running mean std
with open(osp.join(checkdir, '%.5i_ob_rms.pkl' % update), 'wb') as ob_rms_fp:
pickle.dump(env.ob_rms, ob_rms_fp)
with open(osp.join(checkdir, '%.5i_ret_rms.pkl' % update), 'wb') as ret_rms_fp:
pickle.dump(env.ret_rms, ret_rms_fp)
env.close()
def fit(self, paths, targvals):
X = np.concatenate([self._preproc(p) for p in paths])
y = np.concatenate(targvals)
logger.record_tabular("EVBefore", common.explained_variance(self._predict(X), y))
for _ in range(25): self.do_update(X, y)
logger.record_tabular("EVAfter", common.explained_variance(self._predict(X), y))
def learn(network,
env,
seed=None,
nsteps=5,
total_timesteps=int(80e6),
vf_coef=0.5,
ent_coef=0.01,
max_grad_norm=0.5,
lr=7e-4,
lrschedule='linear',
epsilon=1e-5,
alpha=0.99,
gamma=0.99,
log_interval=1000,
load_path=None,
**network_kwargs):
'''
Main entrypoint for A2C algorithm. Train a policy with given network architecture on a given environment using a2c algorithm.
Parameters:
-----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See baselines.common/policies.py/lstm for more details on using recurrent nets in policies
env: RL environment. Should implement interface similar to VecEnv (baselines.common/vec_env) or be wrapped with DummyVecEnv (baselines.common/vec_env/dummy_vec_env.py)
seed: seed to make random number sequence in the alorightm reproducible. By default is None which means seed from system noise generator (not reproducible)
nsteps: int, number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel)
total_timesteps: int, total number of timesteps to train on (default: 80M)
vf_coef: float, coefficient in front of value function loss in the total loss function (default: 0.5)
ent_coef: float, coefficient in front of the policy entropy in the total loss function (default: 0.01)
max_gradient_norm: float, gradient is clipped to have global L2 norm no more than this value (default: 0.5)
lr: float, learning rate for RMSProp (current implementation has RMSProp hardcoded in) (default: 7e-4)
lrschedule: schedule of learning rate. Can be 'linear', 'constant', or a function [0..1] -> [0..1] that takes fraction of the training progress as input and
returns fraction of the learning rate (specified as lr) as output
epsilon: float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5)
alpha: float, RMSProp decay parameter (default: 0.99)
gamma: float, reward discounting parameter (default: 0.99)
log_interval: int, specifies how frequently the logs are printed out (default: 100)
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
'''
set_global_seeds(seed)
# Get the nb of env
nenvs = env.num_envs
policy = build_policy(env, network, **network_kwargs)
# Instantiate the model object (that creates step_model and train_model)
model = Model(policy=policy,
env=env,
nsteps=nsteps,
ent_coef=ent_coef,
vf_coef=vf_coef,
max_grad_norm=max_grad_norm,
lr=lr,
alpha=alpha,
epsilon=epsilon,
total_timesteps=total_timesteps,
lrschedule=lrschedule)
if load_path is not None:
model.load(load_path)
# Instantiate the runner object
runner = Runner(env, model, nsteps=nsteps, gamma=gamma)
# Calculate the batch_size
nbatch = nenvs * nsteps
# Start total timer
tstart = time.time()
total_reward = []
episode_reward = []
for update in range(1, total_timesteps // nbatch + 1):
# Get mini batch of experiences
obs, states, rewards, masks, actions, values = runner.run()
episode_reward = np.append(episode_reward, rewards)
if runner.resetted:
steps = runner.env.envs[0].env._elapsed_steps
corr = len(episode_reward) - steps
episode_reward[:corr] = np.mean(episode_reward[:corr])
total_reward = np.append(total_reward, episode_reward[:corr])
episode_reward = episode_reward[corr:]
policy_loss, value_loss, policy_entropy = model.train(
obs, states, rewards, masks, actions, values)
nseconds = time.time() - tstart
# Calculate the fps (frame per second)
fps = int((update * nbatch) / nseconds)
if update % log_interval == 0 or update == 1:
# Calculates if value function is a good predicator of the returns (ev > 1)
# or if it's just worse than predicting nothing (ev =< 0)
ev = explained_variance(values, rewards)
# logger.record_tabular("nupdates", update)
# logger.record_tabular("rewards per minibatch", rewards)
logger.record_tabular("total_timesteps", update * nbatch)
# logger.record_tabular("fps", fps)
# logger.record_tabular("policy_entropy", float(policy_entropy))
# logger.record_tabular("value_loss", float(value_loss))
# logger.record_tabular("explained_variance", float(ev))
logger.dump_tabular()
with open('rewards.csv', 'a') as csvfile:
rewardwriter = csv.writer(csvfile, delimiter=',')
rewardwriter.writerow(total_reward)
csvfile.close()
plt.plot(total_reward)
plt.xlabel('number of steps')
plt.ylabel('average return per episode')
plt.show()
return model
def learn(policy,
env,
seed,
nsteps=5,
total_timesteps=int(80e6),
vf_coef=0.5,
ent_coef=0.01,
max_grad_norm=0.5,
lr=7e-4,
lrschedule='linear',
epsilon=1e-5,
alpha=0.99,
gamma=0.99,
log_interval=100):
set_global_seeds(seed)
nenvs = env.num_envs
ob_space = env.observation_space
ac_space = env.action_space
model = Model(policy=policy,
ob_space=ob_space,
ac_space=ac_space,
nenvs=nenvs,
nsteps=nsteps,
ent_coef=ent_coef,
vf_coef=vf_coef,
max_grad_norm=max_grad_norm,
lr=lr,
alpha=alpha,
epsilon=epsilon,
total_timesteps=total_timesteps,
lrschedule=lrschedule)
runner = Runner(env, model, nsteps=nsteps, gamma=gamma)
epinfobuf = deque(maxlen=100)
nbatch = nenvs * nsteps
tstart = time.time()
for update in range(1, total_timesteps // nbatch + 1):
obs, states, rewards, masks, actions, values, epinfos = runner.run()
epinfobuf.extend(epinfos)
policy_loss, value_loss, policy_entropy = model.train(
obs, states, rewards, masks, actions, values)
nseconds = time.time() - tstart
fps = int((update * nbatch) / nseconds)
if update % log_interval == 0 or update == 1:
ev = explained_variance(values, rewards)
logger.record_tabular("nupdates", update)
logger.record_tabular("total_timesteps", update * nbatch)
logger.record_tabular("fps", fps)
logger.logkv('eprewmean',
safemean([epinfo['r'] for epinfo in epinfobuf]))
logger.logkv('eplenmean',
safemean([epinfo['l'] for epinfo in epinfobuf]))
logger.record_tabular("policy_entropy", float(policy_entropy))
logger.record_tabular("value_loss", float(value_loss))
logger.record_tabular("explained_variance", float(ev))
logger.logkv('time_elapsed', nseconds)
logger.dump_tabular()
logger.logkv('episodes', runner.episodes_count)
env.close()
return model
def learn(policy,
env,
nsteps=5,
total_episodes=int(10e3),
max_timesteps=int(20e5),
vf_coef=0.5,
ent_coef=0.01,
max_grad_norm=0.5,
lr=7e-4,
lrschedule='linear',
epsilon=1e-5,
alpha=0.99,
gamma=0.99,
save_interval=100,
log_interval=100,
keep_all_ckpt=False):
nenvs = env.num_envs
ob_space = env.observation_space
ac_space = env.action_space
make_model = lambda: Model(policy=policy,
ob_space=ob_space,
ac_space=ac_space,
nenvs=nenvs,
nsteps=nsteps,
ent_coef=ent_coef,
vf_coef=vf_coef,
max_grad_norm=max_grad_norm,
lr=lr,
alpha=alpha,
epsilon=epsilon,
total_timesteps=max_timesteps,
lrschedule=lrschedule)
if save_interval and logger.get_dir():
import cloudpickle
with open(osp.join(logger.get_dir(), 'make_model.pkl'), 'wb') as fh:
fh.write(cloudpickle.dumps(make_model))
model = make_model()
runner = Runner(env, model, nsteps=nsteps, gamma=gamma)
nbatch = nenvs * nsteps
tfirststart = time.time()
update = 0
episodes_so_far = 0
old_savepath = None
while True:
update += 1
if episodes_so_far >= total_episodes:
break
obs, states, rewards, masks, actions, values, num_episodes = runner.run(
)
episodes_so_far += num_episodes
policy_loss, value_loss, policy_entropy = model.train(
obs, states, rewards, masks, actions, values)
nseconds = time.time() - tfirststart
fps = int((update * nbatch) / nseconds)
if update % log_interval == 0 or update == 1:
ev = explained_variance(values, rewards)
logger.record_tabular("nupdates", update)
logger.record_tabular("total_timesteps", update * nbatch)
logger.record_tabular("total_episodes", episodes_so_far)
logger.record_tabular("fps", fps)
logger.record_tabular("policy_entropy", float(policy_entropy))
logger.record_tabular("value_loss", float(value_loss))
logger.record_tabular("explained_variance", float(ev))
logger.record_tabular("time_elapsed", nseconds)
logger.dump_tabular()
if save_interval and logger.get_dir() and (update % save_interval == 0
or update == 1):
checkdir = osp.join(logger.get_dir(), 'checkpoints')
os.makedirs(checkdir, exist_ok=True)
savepath = osp.join(checkdir, '%.5i' % update)
print('Saving to', savepath)
obs_norms = {}
obs_norms['clipob'] = env.clipob
obs_norms['mean'] = env.ob_rms.mean
obs_norms['var'] = env.ob_rms.var + env.epsilon
with open(osp.join(checkdir, 'normalize'), 'wb') as f:
pickle.dump(obs_norms, f, pickle.HIGHEST_PROTOCOL)
model.save(savepath)
if not keep_all_ckpt and old_savepath:
print('Removing previous checkpoint', old_savepath)
os.remove(old_savepath)
old_savepath = savepath
env.close()
def learn(*,
network,
env,
total_timesteps,
sess=None,
fixstd=False,
logstd_init=0,
J_targ=0.01,
batchlim=0.2,
vtrace=0,
eval_env=None,
seed=None,
replay_length=1,
nsteps=20000,
lr=3e-4,
vf_coef=0.5,
gamma=0.99,
lam=0.95,
log_interval=1,
nminibatches=32,
noptepochs=10,
cliprange=0.4,
save_interval=0,
load_path=None,
**network_kwargs):
'''
Learn policy using PPO algorithm (https://arxiv.org/abs/1707.06347)
Parameters:
----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See common/models.py/lstm for more details on using recurrent nets in policies
env: baselines.common.vec_env.VecEnv environment. Needs to be vectorized for parallel environment simulation.
The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class.
nsteps: int number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel)
total_timesteps: int number of timesteps (i.e. number of actions taken in the environment)
lr: float or function learning rate, constant or a schedule function [0,1] -> R+ where 1 is beginning of the
training and 0 is the end of the training.
vf_coef: float value function loss coefficient in the optimization objective
gamma: float discounting factor
lam: float advantage estimation discounting factor (lambda in the paper)
log_interval: int number of timesteps between logging events
nminibatches: int number of training minibatches per update. For recurrent policies,
should be smaller or equal than number of environments run in parallel.
noptepochs: int number of training epochs per update
cliprange: float or function clipping range, constant or schedule function [0,1] -> R+ where 1 is beginning of the training
and 0 is the end of the training
save_interval: int number of timesteps between saving events
load_path: str path to load the model from
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
'''
set_global_seeds(seed)
if isinstance(lr, float): lr = constfn(lr)
else: assert callable(lr)
if isinstance(cliprange, float): cliprange = constfn(cliprange)
else: assert callable(cliprange)
total_timesteps = int(total_timesteps)
policy = build_policy(env, network, **network_kwargs)
# Get the nb of env
nenvs = env.num_envs
# Get state_space and action_space
ob_space = env.observation_space
ac_space = env.action_space
acdim = ac_space.shape[0]
# Calculate the batch_size
nbatch = nenvs * nsteps
nbatch_train = nbatch // nminibatches
# Instantiate the model object (that creates act_model and train_model)
make_model = lambda: Model(policy=policy,
ob_space=ob_space,
ac_space=ac_space,
nbatch_act=nenvs,
nbatch_train=nbatch_train,
nsteps=nsteps,
vf_coef=vf_coef)
model = make_model()
if load_path is not None:
model.load(load_path)
# Instantiate the runner object
runner = Runner(env=env, model=model, nsteps=nsteps, gamma=gamma, lam=lam)
if eval_env is not None:
eval_runner = EvalRunner(env=eval_env,
model=model,
nsteps=5000,
gamma=gamma,
lam=lam)
eval_runner.obfilt = runner.obfilt
eval_runner.rewfilt = runner.rewfilt
epinfobuf = deque(maxlen=10)
if eval_env is not None:
eval_epinfobuf = deque(maxlen=10)
# Start total timer
tfirststart = time.time()
nupdates = total_timesteps // nbatch
def add_vtarg_and_adv(seg, gamma, value, lam):
"""
Compute target value using TD(lambda) estimator, and advantage with GAE(lambda)
"""
done = np.append(
seg["done"], 0
) # last element is only used for last vtarg, but we already zeroed it if last new = 1
T = len(seg["rew"])
gaelam = np.empty(T, 'float32')
rew = runner.rewfilt(seg["rew"])
lastgaelam = 0
for t in reversed(range(T)):
nonterminal = 1 - done[t + 1]
delta = rew[t] + gamma * value[t + 1] * nonterminal - value[t]
gaelam[
t] = lastgaelam = delta + gamma * lam * nonterminal * lastgaelam
ret = gaelam + value[:-1]
return ret, gaelam
def add_vtarg_and_adv_vtrace(seg, gamma, value, rho):
"""
Compute target value using TD(lambda) estimator, and advantage with GAE(lambda)
"""
done = np.append(
seg["done"], 0
) # last element is only used for last vtarg, but we already zeroed it if last new = 1
rho_ = np.append(rho, 1.0)
r = np.minimum(1.0, rho_)
T = len(seg["rew"])
gaelam = np.empty(T, 'float32')
rew = runner.rewfilt(seg["rew"])
lastgaelam = 0
for t in reversed(range(T)):
nonterminal = 1 - done[t + 1]
delta = (rew[t] + gamma * value[t + 1] * nonterminal - value[t])
gaelam[t] = delta + gamma * lam * nonterminal * lastgaelam
lastgaelam = r[t] * gaelam[t]
ret = r[:-1] * gaelam + value[:-1]
return ret, gaelam
seg = None
cliprangenow = cliprange(1.0)
alpha_IS = 1.0
logstd_var = None
for var in tf.trainable_variables():
if 'logstd' in var.name:
logstd_var = var
sess.run(logstd_var.assign(logstd_init * np.ones(shape=(1, acdim))))
for update in range(1, nupdates + 1):
assert nbatch % nminibatches == 0
# Start timer
tstart = time.time()
frac = 1.0 - (update - 1.0) / nupdates
# Calculate the learning rate
lrnow = np.maximum(1e-4, lr(frac))
# Calculate the cliprange
if fixstd:
frac_ = np.maximum(1.0 - (update - 1.0) / nupdates, 0)
sess.run(
logstd_var.assign(((logstd_init + 1) * frac_ - 1) *
np.ones(shape=(1, acdim))))
# Get minibatch
if seg is None:
prev_seg = seg
seg = {}
else:
prev_seg = {}
for i in seg:
prev_seg[i] = np.copy(seg[i])
seg["ob"], seg["rew"], seg["done"], seg["ac"], seg["neglogp"], seg[
"mean"], seg[
"logstd"], final_obs, final_done, epinfos = runner.run() #pylint: disable=E0632
# print(np.shape(seg["ob"]))
if prev_seg is not None:
for key in seg:
if len(np.shape(seg[key])) == 1:
seg[key] = np.hstack([prev_seg[key], seg[key]])
else:
seg[key] = np.vstack([prev_seg[key], seg[key]])
if np.shape(seg[key])[0] > replay_length * nsteps:
seg[key] = seg[key][-replay_length * nsteps:]
obs = seg["ob"]
edge_s = np.transpose(obs)[:40].reshape(-1, 8, len(obs))
cloud_s = np.transpose(obs)[40:]
edge_queue = edge_s[2] # shape (episode length, 8)
edge_cpu = edge_s[3]
workload = edge_s[4]
cloud_queue = cloud_s[2]
cloud_cpu = cloud_s[3]
edge_queue_avg = edge_queue.mean() # shape (,)
cloud_queue_avg = cloud_queue.mean() # float
edge_power = 10 * (edge_cpu.sum(axis=0) *
(10**9) / 10)**3 # shape (5000,)
cloud_power = 54 * (cloud_cpu * (10**9) / 54)**3 # shape (5000,)
edge_power_avg = edge_power.mean()
cloud_power_avg = cloud_power.mean()
power = edge_power_avg + cloud_power_avg
ob_stack = np.vstack([seg["ob"], final_obs])
values = model.values(runner.obfilt(ob_stack))
values[-1] = (1.0 - final_done) * values[-1]
ob = runner.obfilt(seg["ob"])
mean_now, logstd_now = model.meanlogstds(ob)
neglogpnow = 0.5 * np.sum(np.square((seg["ac"] - mean_now) / np.exp(logstd_now)), axis=-1) \
+ 0.5 * np.log(2.0 * np.pi) * np.shape(seg["ac"])[1] \
+ np.sum(logstd_now, axis=-1)
rho = np.exp(-neglogpnow + seg["neglogp"])
if vtrace == 1:
ret, gae = add_vtarg_and_adv_vtrace(seg, gamma, values, rho)
else:
ret, gae = add_vtarg_and_adv(seg, gamma, values, lam)
if eval_env is not None:
eval_obs, eval_returns, eval_masks, eval_actions, _, _, eval_epinfos = eval_runner.run(
)
eval_edge_s = np.transpose(eval_obs)[:40].reshape(
-1, 8, len(eval_obs))
eval_cloud_s = np.transpose(eval_obs)[40:]
eval_edge_queue = eval_edge_s[2] # shape (episode length, 8)
eval_edge_cpu = eval_edge_s[3]
eval_workload = eval_edge_s[4]
eval_cloud_queue = eval_cloud_s[2]
eval_cloud_cpu = eval_cloud_s[3]
eval_edge_queue_avg = eval_edge_queue.mean() # shape (,)
eval_cloud_queue_avg = eval_cloud_queue.mean() # float
eval_edge_power = 10 * (eval_edge_cpu.sum(axis=0) *
(10**9) / 10)**3 # shape (5000,)
eval_cloud_power = 54 * (eval_cloud_cpu *
(10**9) / 54)**3 # shape (5000,)
eval_edge_power_avg = eval_edge_power.mean()
eval_cloud_power_avg = eval_cloud_power.mean()
eval_power = eval_edge_power_avg + eval_cloud_power_avg
prior_row = np.zeros(len(seg["ob"]))
rho_dim = np.exp(- 0.5 * np.square((seg["ac"] - mean_now) / np.exp(logstd_now)) \
- logstd_now + 0.5 * np.square((seg["ac"] - seg["mean"]) / np.exp(seg["logstd"]))\
+ seg["logstd"])
temp_prior = []
for i in range(int(len(prior_row) / nsteps)):
temp_row = np.mean(
np.abs(rho_dim[i * nsteps:(i + 1) * nsteps] - 1.0) + 1.0)
# local_rho[i + (replay_length-int(len(prior_row)/nsteps))].append(temp_row)
if temp_row > 1 + batchlim:
prior_row[i * nsteps:(i + 1) * nsteps] = 0
else:
prior_row[i * nsteps:(i + 1) * nsteps] = 1
# prior_row[i * nsteps:(i + 1) * nsteps] = 1
temp_prior.append(temp_row)
epinfobuf.extend(epinfos)
if eval_env is not None:
eval_epinfobuf.extend(eval_epinfos)
# Here what we're going to do is for each minibatch calculate the loss and append it.
mblossvals = []
# Index of each element of batch_size
# Create the indices array
inds1 = np.arange(len(seg["ob"]) - nsteps)
inds2 = np.arange(nsteps) + len(seg["ob"]) - nsteps
nbatch_adapt1 = int(
(np.sum(prior_row) - nsteps) / nsteps * nbatch_train)
nbatch_adapt2 = int((nsteps) / nsteps * nbatch_train)
idx1 = []
idx2 = []
kl_rest = np.ones(len(seg["ob"])) * np.sum(prior_row) / nsteps
kl_rest[:-nsteps] = 0
# print(kl_rest)
for _ in range(noptepochs):
losses_epoch = []
for _ in range(int(nsteps / nbatch_train)):
if nbatch_adapt1 > 0:
idx1 = np.random.choice(inds1,
nbatch_adapt1,
p=prior_row[:-nsteps] /
np.sum(prior_row[:-nsteps]))
idx2 = np.random.choice(inds2, nbatch_adapt2)
idx = np.hstack([idx1, idx2]).astype(int)
slices = (arr[idx]
for arr in (ob, ret, gae, seg["done"], seg["ac"],
values[:-1], seg["neglogp"], seg["mean"],
seg["logstd"], kl_rest, rho, neglogpnow))
loss_epoch = model.train(lrnow, cliprangenow, alpha_IS,
*slices)
mblossvals.append(loss_epoch)
losses_epoch.append(loss_epoch)
# Feedforward --> get losses --> update
lossvals = np.mean(mblossvals, axis=0)
# Update adaptive IS target constant
print("IS loss avg :", lossvals[3])
if lossvals[3] > J_targ * 1.5:
alpha_IS *= 2
print("IS target const is increased")
elif lossvals[3] < J_targ / 1.5:
alpha_IS /= 2
print("IS target const is reduced")
alpha_IS = np.clip(alpha_IS, 2**(-10), 64)
# End timer
tnow = time.time()
# Calculate the fps (frame per second)
fps = int(nbatch / (tnow - tstart))
if update % log_interval == 0 or update == 1:
# Calculates if value function is a good predicator of the returns (ev > 1)
# or if it's just worse than predicting nothing (ev =< 0)
ev = explained_variance(values[:-1], ret)
logger.logkv("IS target const", alpha_IS)
logger.logkv("clipping factor", cliprangenow)
logger.logkv("learning rate", lrnow)
logger.logkv("serial_timesteps", update * nsteps)
logger.logkv("nupdates", update)
logger.logkv("total_timesteps", update * nbatch)
logger.logkv("fps", fps)
logger.logkv("explained_variance", float(ev))
logger.logkv('eprewmean',
safemean([epinfo['r'] for epinfo in epinfobuf]))
logger.logkv('eplenmean',
safemean([epinfo['l'] for epinfo in epinfobuf]))
logger.logkv("edge_queue_avg", edge_queue_avg)
logger.logkv("power", power)
if eval_env is not None:
logger.logkv(
'eval_eprewmean',
safemean([epinfo['r'] for epinfo in eval_epinfos]))
logger.logkv(
'eval_eplenmean',
safemean([epinfo['l'] for epinfo in eval_epinfos]))
logger.logkv("eval_edge_queue_avg", eval_edge_queue_avg)
logger.logkv("eval_power", eval_power)
logger.logkv('time_elapsed', tnow - tfirststart)
for (lossval, lossname) in zip(lossvals, model.loss_names):
logger.logkv(lossname, lossval)
if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
logger.dumpkvs()
if save_interval and (update % save_interval == 0
or update == 1) and logger.get_dir() and (
MPI is None
or MPI.COMM_WORLD.Get_rank() == 0):
checkdir = osp.join(logger.get_dir(), 'checkpoints')
os.makedirs(checkdir, exist_ok=True)
savepath = osp.join(checkdir, '%.5i' % update)
print('Saving to', savepath)
model.save(savepath)
return model
def learn(
env,
policy_fn,
*,
timesteps_per_batch, # what to train on
epsilon,
beta,
cg_iters,
gamma,
lam, # advantage estimation
entcoeff=0.0,
cg_damping=1e-2,
vf_stepsize=3e-4,
vf_iters=3,
max_timesteps=0,
max_episodes=0,
max_iters=0, # time constraint
callback=None,
TRPO=False,
n_policy=1,
policy_type=0,
filepath='',
session,
retrace=False):
'''
:param TRPO: True: TRPO, False: COPOS
:param n_policy: Number of periodic policy parts
:param policy_type: 0: Optimize 'n_policy' policies that are executed periodically. All the policies are updated.
1: The last 'n_policy' policies are executed periodically but only the last one is optimized.
2: The policy is spread over 'n_policy' time steps.
'''
nworkers = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
np.set_printoptions(precision=3)
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
pis = [
policy_fn("pi_" + str(i), ob_space, ac_space) for i in range(n_policy)
]
oldpis = [
policy_fn("oldpi_" + str(i), ob_space, ac_space)
for i in range(n_policy)
]
pi_vf = policy_fn("pi_vf", ob_space, ac_space)
atarg = tf.placeholder(
dtype=tf.float32,
shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
ob = U.get_placeholder_cached(name="ob")
if policy_type == 0:
print(
"Policy type: " + str(policy_type) +
". Optimize 'n_policy' policies that are executed periodically. All the policies are updated."
)
elif policy_type == 1:
print(
"Policy type: " + str(policy_type) +
". The last 'n_policy' policies are executed periodically but only the last one is optimized."
)
elif policy_type == 2:
print("Policy type: " + str(policy_type) +
". The policy is spread over 'n_policy' time steps.")
else:
print("Policy type: " + str(policy_type) + " is not supported.")
# Compute variables for each policy separately
old_entropy = []
get_flat = []
set_from_flat = []
assign_old_eq_new = []
copy_policy_back = []
compute_losses = []
compute_lossandgrad = []
compute_fvp = []
for i in range(n_policy):
pi = pis[i]
oldpi = oldpis[i]
ac = pi.pdtype.sample_placeholder([None])
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
old_entropy.append(oldpi.pd.entropy())
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
entbonus = entcoeff * meanent
ratio = tf.exp(pi.pd.logp(ac) -
oldpi.pd.logp(ac)) # advantage * pnew / pold
if retrace:
surrgain = tf.reduce_mean(
atarg) # atarg incorporates pnew / pold already
else:
surrgain = tf.reduce_mean(ratio * atarg)
optimgain = surrgain + entbonus
losses = [optimgain, meankl, entbonus, surrgain, meanent]
loss_names = ["optimgain", "meankl", "entloss", "surrgain", "Entropy"]
dist = meankl
all_var_list = pi.get_trainable_variables()
all_var_list = [
v for v in all_var_list if v.name.split("/")[0].startswith("pi")
]
var_list = [
v for v in all_var_list if v.name.split("/")[1].startswith("pol")
]
#
# fvp: Fisher Information Matrix / vector product based on Hessian of KL-divergence
# fvp = F * v, where F = - E \partial_1 \partial_2 KL_div(p1 || p2)
#
get_flat.append(U.GetFlat(var_list))
set_from_flat.append(U.SetFromFlat(var_list))
klgrads = tf.gradients(dist, var_list)
flat_tangent = tf.placeholder(dtype=tf.float32,
shape=[None],
name="flat_tan")
shapes = [var.get_shape().as_list() for var in var_list]
start = 0
tangents = []
for shape in shapes:
sz = U.intprod(shape)
tangents.append(tf.reshape(flat_tangent[start:start + sz], shape))
start += sz
gvp = tf.add_n([
tf.reduce_sum(g * tangent)
for (g, tangent) in zipsame(klgrads, tangents)
]) #pylint: disable=E1111
fvp = U.flatgrad(gvp, var_list)
#
# fvpll: Fisher Information Matrix / vector product based on exact FIM
# fvpll = F * v, where F = E[\partial_1 \log p * \partial_2 \log p]
#
# Mean: (\partial \mu^T / \partial param1) * Precision * (\partial \mu / \partial param1)
# Covariance: 0.5 * Trace[Precision * (\partial Cov / \partial param1) *
# Precision * (\partial Cov / \partial param2)]
if i > 0:
# Only needed for policy_type == 1 for copying policy 'i' to policy 'i-1'
copy_policy_back.append(
U.function(
[], [],
updates=[
tf.assign(oldv, newv) for (oldv, newv) in zipsame(
pis[i - 1].get_variables(), pi.get_variables())
]))
assign_old_eq_new.append(
U.function([], [],
updates=[
tf.assign(oldv, newv) for (oldv, newv) in zipsame(
oldpi.get_variables(), pi.get_variables())
]))
compute_losses.append(U.function([ob, ac, atarg], losses))
compute_lossandgrad.append(
U.function([ob, ac, atarg],
losses + [U.flatgrad(optimgain, var_list)]))
compute_fvp.append(U.function([flat_tangent, ob, ac, atarg], fvp))
# Value function is global to all policies
vferr = tf.reduce_mean(tf.square(pi_vf.vpred - ret))
all_var_list = pi_vf.get_trainable_variables()
vf_var_list = [
v for v in all_var_list if v.name.split("/")[1].startswith("vf")
]
vfadam = MpiAdam(vf_var_list)
compute_vflossandgrad = U.function([ob, ret],
U.flatgrad(vferr, vf_var_list))
@contextmanager
def timed(msg):
if rank == 0:
print(colorize(msg, color='magenta'))
tstart = time.time()
yield
print(
colorize("done in %.3f seconds" % (time.time() - tstart),
color='magenta'))
else:
yield
def allmean(x):
assert isinstance(x, np.ndarray)
out = np.empty_like(x)
MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
out /= nworkers
return out
U.initialize()
if policy_type == 1:
# Initialize policies to identical values
th_init = get_flat[0]()
for i in range(n_policy):
MPI.COMM_WORLD.Bcast(th_init, root=0)
set_from_flat[i](th_init)
vfadam.sync()
print("Init param sum", th_init.sum(), flush=True)
else:
for i in range(n_policy):
th_init = get_flat[i]()
MPI.COMM_WORLD.Bcast(th_init, root=0)
set_from_flat[i](th_init)
vfadam.sync()
print("Init param sum", th_init.sum(), flush=True)
# Initialize eta, omega optimizer
init_eta = 0.5
init_omega = 2.0
eta_omega_optimizer = EtaOmegaOptimizer(beta, epsilon, init_eta,
init_omega)
# Prepare for rollouts
# ----------------------------------------
seg_gen = []
for i in range(len(pis)):
seg_gen.append(
traj_segment_generator(pis,
i + 1,
pi_vf,
env,
timesteps_per_batch,
stochastic=True))
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=40) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=40) # rolling buffer for episode rewards
assert sum([max_iters > 0, max_timesteps > 0, max_episodes > 0]) == 1
n_saves = 0
while True:
if callback: callback(locals(), globals())
if max_timesteps and timesteps_so_far >= max_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
logger.log("********** Iteration %i ************" % iters_so_far)
if max_timesteps > 0 and (timesteps_so_far >=
(n_saves * max_timesteps // 5)):
# Save policy
saver = tf.train.Saver()
saver.save(session, filepath + "_" + str(iters_so_far))
n_saves += 1
with timed("sampling"):
if policy_type == 1 and iters_so_far < len(pis):
all_seg = seg_gen[iters_so_far].__next__(
) # For four time steps use the four policies
else:
all_seg = seg_gen[-1].__next__()
if policy_type == 1 and retrace:
act_pi_ids = np.empty_like(all_seg["vpred"], dtype=int)
act_pi_ids[:] = n_policy - 1 # Always update the last policy
add_vtarg_and_adv_retrace(all_seg, gamma, lam, act_pi_ids)
else:
add_vtarg_and_adv(all_seg, gamma, lam)
# Split the advantage functions etc. among the policies
segs = split_traj_segment(pis, all_seg)
# Update all policies
for pi_id in range(n_policy):
if policy_type == 1:
# Update only last policy
pi_id = n_policy - 1
# Using all the samples
seg = all_seg
else:
seg = segs[pi_id]
pi = pis[pi_id]
oldpi = oldpis[pi_id]
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg[
"tdlamret"]
vpredbefore = seg[
"vpred"] # predicted value function before update
atarg = (atarg - atarg.mean()) / atarg.std(
) # standardized advantage function estimate
if hasattr(pi, "ret_rms"): pi.ret_rms.update(tdlamret)
if hasattr(pi, "ob_rms"):
pi.ob_rms.update(ob) # update running mean/std for policy
args = seg["ob"], seg["ac"], atarg
fvpargs = [arr[::5] for arr in args]
def fisher_vector_product(p):
return allmean(compute_fvp[pi_id](p, *
fvpargs)) + cg_damping * p
assign_old_eq_new[pi_id](
) # set old parameter values to new parameter values
with timed("computegrad"):
*lossbefore, g = compute_lossandgrad[pi_id](*args)
lossbefore = allmean(np.array(lossbefore))
g = allmean(g)
if np.allclose(g, 0):
logger.log("Got zero gradient. not updating")
if policy_type == 1:
# Update only the last policy
break
else:
with timed("cg"):
stepdir = cg(fisher_vector_product,
g,
cg_iters=cg_iters,
verbose=rank == 0)
assert np.isfinite(stepdir).all()
if TRPO:
#
# TRPO specific code.
# Find correct step size using line search
#
shs = .5 * stepdir.dot(fisher_vector_product(stepdir))
lm = np.sqrt(shs / epsilon)
# logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g))
fullstep = stepdir / lm
expectedimprove = g.dot(fullstep)
surrbefore = lossbefore[0]
stepsize = 1.0
thbefore = get_flat[pi_id]()
for _ in range(10):
thnew = thbefore + fullstep * stepsize
set_from_flat[pi_id](thnew)
meanlosses = surr, kl, *_ = allmean(
np.array(compute_losses[pi_id](*args)))
improve = surr - surrbefore
logger.log("Expected: %.3f Actual: %.3f" %
(expectedimprove, improve))
if not np.isfinite(meanlosses).all():
logger.log(
"Got non-finite value of losses -- bad!")
elif kl > epsilon * 1.5:
logger.log(
"violated KL constraint. shrinking step.")
elif improve < 0:
logger.log(
"surrogate didn't improve. shrinking step.")
else:
logger.log("Stepsize OK!")
break
stepsize *= .5
else:
logger.log("couldn't compute a good step")
set_from_flat[pi_id](thbefore)
else:
#
# COPOS specific implementation.
#
copos_update_dir = stepdir
# Split direction into log-linear 'w_theta' and non-linear 'w_beta' parts
w_theta, w_beta = pi.split_w(copos_update_dir)
# q_beta(s,a) = \grad_beta \log \pi(a|s) * w_beta
# = features_beta(s) * K^T * Prec * a
# q_beta = self.target.get_q_beta(features_beta, actions)
Waa, Wsa = pi.w2W(w_theta)
wa = pi.get_wa(ob, w_beta)
varphis = pi.get_varphis(ob)
# Optimize eta and omega
tmp_ob = np.zeros(
(1, ) + env.observation_space.shape
) # We assume that entropy does not depend on the NN
old_ent = old_entropy[pi_id].eval({oldpi.ob: tmp_ob})[0]
eta, omega = eta_omega_optimizer.optimize(
w_theta, Waa, Wsa, wa, varphis, pi.get_kt(),
pi.get_prec_matrix(), pi.is_new_policy_valid, old_ent)
logger.log("Initial eta: " + str(eta) + " and omega: " +
str(omega))
current_theta_beta = get_flat[pi_id]()
prev_theta, prev_beta = pi.all_to_theta_beta(
current_theta_beta)
for i in range(2):
# Do a line search for both theta and beta parameters by adjusting only eta
eta = eta_search(w_theta, w_beta, eta, omega, allmean,
compute_losses[pi_id],
get_flat[pi_id], set_from_flat[pi_id],
pi, epsilon, args)
logger.log("Updated eta, eta: " + str(eta) +
" and omega: " + str(omega))
# Find proper omega for new eta. Use old policy parameters first.
set_from_flat[pi_id](pi.theta_beta_to_all(
prev_theta, prev_beta))
eta, omega = \
eta_omega_optimizer.optimize(w_theta, Waa, Wsa, wa, varphis, pi.get_kt(),
pi.get_prec_matrix(), pi.is_new_policy_valid, old_ent, eta)
logger.log("Updated omega, eta: " + str(eta) +
" and omega: " + str(omega))
# Use final policy
logger.log("Final eta: " + str(eta) + " and omega: " +
str(omega))
cur_theta = (eta * prev_theta +
w_theta.reshape(-1, )) / (eta + omega)
cur_beta = prev_beta + w_beta.reshape(-1, ) / eta
thnew = pi.theta_beta_to_all(cur_theta, cur_beta)
set_from_flat[pi_id](thnew)
meanlosses = surr, kl, *_ = allmean(
np.array(compute_losses[pi_id](*args)))
if nworkers > 1 and iters_so_far % 20 == 0:
paramsums = MPI.COMM_WORLD.allgather(
(thnew.sum(),
vfadam[pi_id].getflat().sum())) # list of tuples
assert all(
np.allclose(ps, paramsums[0]) for ps in paramsums[1:])
for (lossname, lossval) in zip(loss_names, meanlosses):
logger.record_tabular(lossname, lossval)
logger.record_tabular(
"ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
if policy_type == 1:
# Update only the last policy
break
if policy_type == 1:
# Copy policies 1, ..., i to 0, ..., i-1
for j in range(n_policy - 1):
copy_policy_back[j]()
with timed("vf"):
for _ in range(vf_iters):
for (mbob, mbret) in dataset.iterbatches(
(all_seg["ob"], all_seg["tdlamret"]),
include_final_partial_batch=False,
batch_size=64):
g = allmean(compute_vflossandgrad(mbob, mbret))
vfadam.update(g, vf_stepsize)
lrlocal = (all_seg["ep_lens"], all_seg["ep_rets"]) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("AverageReturn", np.mean(rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
if rank == 0:
logger.dump_tabular()
def learn(self):
# Prepare for rollouts
# ----------------------------------------
seg_gen = self.temp_seg_gen
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=40) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=40) # rolling buffer for episode rewards
assert sum([
self.max_iters > 0, self.max_timesteps > 0, self.max_episodes > 0
]) == 1
while True:
if self.max_timesteps and timesteps_so_far >= self.max_timesteps:
break
elif self.max_episodes and episodes_so_far >= self.max_episodes:
break
elif self.max_iters and iters_so_far >= self.max_iters:
break
elif self.max_epi_avg and len(lenbuffer) > 0 and np.mean(
lenbuffer) >= self.max_epi_avg:
break
logger.log("********** Iteration %i ************" % iters_so_far)
with self.timed("sampling"):
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, self.gamma, self.lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
self.ob, self.ac, self.atarg, self.tdlamret = seg["ob"], seg[
"ac"], seg["adv"], seg["tdlamret"]
self.vpredbefore = seg[
"vpred"] # predicted value function before udpate
self.atarg = (self.atarg - self.atarg.mean()) / self.atarg.std(
) # standardized advantage function estimate
if hasattr(self.pi, "ret_rms"):
self.pi.ret_rms.update(self.tdlamret)
if hasattr(self.pi, "ob_rms"):
self.pi.ob_rms.update(
self.ob) # update running mean/std for policy
args = seg["ob"], seg["ac"], self.atarg
self.fvpargs = [arr[::5] for arr in args]
self.assign_old_eq_new(
) # set old parameter values to new parameter values
with self.timed("computegrad"):
*lossbefore, g = self.compute_lossandgrad(*args)
lossbefore = self.allmean(np.array(lossbefore))
g = self.allmean(g)
if np.allclose(g, 0):
logger.log("Got zero gradient. not updating")
else:
with self.timed("cg"):
stepdir = cg(self.fisher_vector_product,
g,
cg_iters=self.cg_iters,
verbose=self.rank == 0)
assert np.isfinite(stepdir).all()
shs = .5 * stepdir.dot(self.fisher_vector_product(stepdir))
lm = np.sqrt(shs / self.max_kl)
# logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g))
fullstep = stepdir / lm
expectedimprove = g.dot(fullstep)
surrbefore = lossbefore[0]
stepsize = 1.0
thbefore = self.get_flat()
for _ in range(10):
thnew = thbefore + fullstep * stepsize
self.set_from_flat(thnew)
meanlosses = surr, kl, *_ = self.allmean(
np.array(self.compute_losses(*args)))
improve = surr - surrbefore
logger.log("Expected: %.3f Actual: %.3f" %
(expectedimprove, improve))
if not np.isfinite(meanlosses).all():
logger.log("Got non-finite value of losses -- bad!")
elif kl > self.max_kl * 1.5:
logger.log("violated KL constraint. shrinking step.")
elif improve < 0:
logger.log("surrogate didn't improve. shrinking step.")
else:
logger.log("Stepsize OK!")
break
stepsize *= .5
else:
logger.log("couldn't compute a good step")
self.set_from_flat(thbefore)
if self.nworkers > 1 and iters_so_far % 20 == 0:
paramsums = MPI.COMM_WORLD.allgather(
(thnew.sum(),
self.vfadam.getflat().sum())) # list of tuples
assert all(
np.allclose(ps, paramsums[0]) for ps in paramsums[1:])
for (lossname, lossval) in zip(self.loss_names, meanlosses):
logger.record_tabular(lossname, lossval)
with self.timed("vf"):
for _ in range(self.vf_iters):
for (mbob, mbret) in dataset.iterbatches(
(seg["ob"], seg["tdlamret"]),
include_final_partial_batch=False,
batch_size=64):
g = self.allmean(
self.compute_vflossandgrad(mbob, mbret))
self.vfadam.update(g, self.vf_stepsize)
logger.record_tabular(
"ev_tdlam_before",
explained_variance(self.vpredbefore, self.tdlamret))
lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
if self.rank == 0:
logger.dump_tabular()
def update(self, lr, cliprange):
# fill rollout buffers
self.runner.rollout()
## TODO: normalized rewards
# coinrun does NOT normalize its rewards
if self.normrew:
rffs = np.array(
[self.rff.update(rew) for rew in self.runner.buf_rews.T])
# print('optimizers.py, class PPO, def update, rffs.shape: {}'.format(rffs.shape))
rffs_mean, rffs_std, rffs_count = utils.get_moments(rffs.ravel())
self.rff_rms.update_from_moments(rffs_mean, rffs_std**2,
rffs_count)
rews = self.runner.buf_rews / np.sqrt(self.rff_rms.var)
else:
rews = np.copy(self.runner.buf_rews)
self.calculate_advantages(rews=rews,
use_news=self.use_news,
gamma=self.gamma,
lam=self.lam)
# this is a little bit different than the original coinrun implementation
# they only normalize advantages using batch mean and std instead of
# entire data & we add 1e-7 instead of 1e-8
if self.normadv:
mean, std = np.mean(self.buf_advs), np.std(self.buf_advs)
self.buf_advs = (self.buf_advs - mean) / (std + 1e-7)
## this only works for non-recurrent version
nbatch = self.nenvs * self.nsteps
nbatch_train = nbatch // self.nminibatches
# BUG FIXED: np.arange(nbatch_train) to np.arange(nbatch)
# might be the cause of unstable training performance
train_idx = np.arange(nbatch)
# another thing is that they completely shuffle the experiences
# flatten axes 0 and 1 (we do not swap)
def f01(x):
sh = x.shape
return x.reshape(sh[0] * sh[1], *sh[2:])
flattened_obs = f01(self.runner.buf_obs)
ph_buf = [(self.policy.ph_ob, flattened_obs),
(self.policy.ph_ac, f01(self.runner.buf_acs)),
(self.ph_oldvpred, f01(self.runner.buf_vpreds)),
(self.ph_oldnlp, f01(self.runner.buf_nlps)),
(self.ph_ret, f01(self.buf_rets)),
(self.ph_adv, f01(self.buf_advs))]
# when we begin to work with curiosity, we might need make a couple of
# changes to this training strategy
mblossvals = []
for e in range(self.nepochs):
np.random.shuffle(train_idx)
for start in range(0, nbatch, nbatch_train):
end = start + nbatch_train
mbidx = train_idx[start:end]
fd = {ph: buf[mbidx] for (ph, buf) in ph_buf}
fd.update({self.ph_lr: lr, self.ph_cliprange: cliprange})
mblossvals.append(sess().run(self._losses + (self._train, ),
feed_dict=fd)[:-1])
mblossvals = [mblossvals[0]]
info = dict(
advmean=self.buf_advs.mean(),
advstd=self.buf_advs.std(),
retmean=self.buf_rets.mean(),
retstd=self.buf_rets.std(),
vpredmean=self.runner.buf_vpreds.mean(),
vpredstd=self.runner.buf_vpreds.std(),
ev=explained_variance(self.runner.buf_vpreds.ravel(),
self.buf_rets.ravel()),
rew_mean=np.mean(self.runner.buf_rews),
)
info.update(
zip(['opt_' + ln for ln in self.loss_names],
np.mean([mblossvals[0]], axis=0)))
return info
def learn(*,
network,
env,
total_timesteps,
timesteps_per_batch=1024, # what to train on
max_kl=0.001,
cg_iters=10,
gamma=0.99,
lam=1.0, # advantage estimation
seed=None,
ent_coef=0.0,
cg_damping=1e-2,
vf_stepsize=3e-4,
vf_iters =3,
max_episodes=0, max_iters=0, # time constraint
callback=None,
load_path=None,
**network_kwargs
):
'''
learn a policy function with TRPO algorithm
Parameters:
----------
network neural network to learn. Can be either string ('mlp', 'cnn', 'lstm', 'lnlstm' for basic types)
or function that takes input placeholder and returns tuple (output, None) for feedforward nets
or (output, (state_placeholder, state_output, mask_placeholder)) for recurrent nets
env environment (one of the gym environments or wrapped via baselines.common.vec_env.VecEnv-type class
timesteps_per_batch timesteps per gradient estimation batch
max_kl max KL divergence between old policy and new policy ( KL(pi_old || pi) )
ent_coef coefficient of policy entropy term in the optimization objective
cg_iters number of iterations of conjugate gradient algorithm
cg_damping conjugate gradient damping
vf_stepsize learning rate for adam optimizer used to optimie value function loss
vf_iters number of iterations of value function optimization iterations per each policy optimization step
total_timesteps max number of timesteps
max_episodes max number of episodes
max_iters maximum number of policy optimization iterations
callback function to be called with (locals(), globals()) each policy optimization step
load_path str, path to load the model from (default: None, i.e. no model is loaded)
**network_kwargs keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
Returns:
-------
learnt model
'''
if MPI is not None:
nworkers = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
else:
nworkers = 1
rank = 0
cpus_per_worker = 1
U.get_session(config=tf.ConfigProto(
allow_soft_placement=True,
inter_op_parallelism_threads=cpus_per_worker,
intra_op_parallelism_threads=cpus_per_worker
))
policy = build_policy(env, network, value_network='copy', **network_kwargs)
set_global_seeds(seed)
np.set_printoptions(precision=3)
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
ob = observation_placeholder(ob_space)
with tf.variable_scope("pi"):
pi = policy(observ_placeholder=ob)
with tf.variable_scope("oldpi"):
oldpi = policy(observ_placeholder=ob)
atarg = tf.placeholder(dtype=tf.float32, shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
ac = pi.pdtype.sample_placeholder([None])
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
entbonus = ent_coef * meanent
vferr = tf.reduce_mean(tf.square(pi.vf - ret))
ratio = tf.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac)) # advantage * pnew / pold
surrgain = tf.reduce_mean(ratio * atarg)
optimgain = surrgain + entbonus
losses = [optimgain, meankl, entbonus, surrgain, meanent]
loss_names = ["optimgain", "meankl", "entloss", "surrgain", "entropy"]
dist = meankl
all_var_list = get_trainable_variables("pi")
# var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("pol")]
# vf_var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("vf")]
var_list = get_pi_trainable_variables("pi")
vf_var_list = get_vf_trainable_variables("pi")
vfadam = MpiAdam(vf_var_list)
get_flat = U.GetFlat(var_list)
set_from_flat = U.SetFromFlat(var_list)
klgrads = tf.gradients(dist, var_list)
flat_tangent = tf.placeholder(dtype=tf.float32, shape=[None], name="flat_tan")
shapes = [var.get_shape().as_list() for var in var_list]
start = 0
tangents = []
for shape in shapes:
sz = U.intprod(shape)
tangents.append(tf.reshape(flat_tangent[start:start+sz], shape))
start += sz
gvp = tf.add_n([tf.reduce_sum(g*tangent) for (g, tangent) in zipsame(klgrads, tangents)]) #pylint: disable=E1111
fvp = U.flatgrad(gvp, var_list)
assign_old_eq_new = U.function([],[], updates=[tf.assign(oldv, newv)
for (oldv, newv) in zipsame(get_variables("oldpi"), get_variables("pi"))])
compute_losses = U.function([ob, ac, atarg], losses)
compute_lossandgrad = U.function([ob, ac, atarg], losses + [U.flatgrad(optimgain, var_list)])
compute_fvp = U.function([flat_tangent, ob, ac, atarg], fvp)
compute_vflossandgrad = U.function([ob, ret], U.flatgrad(vferr, vf_var_list))
@contextmanager
def timed(msg):
if rank == 0:
print(colorize(msg, color='magenta'))
tstart = time.time()
yield
print(colorize("done in %.3f seconds"%(time.time() - tstart), color='magenta'))
else:
yield
def allmean(x):
assert isinstance(x, np.ndarray)
if MPI is not None:
out = np.empty_like(x)
MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
out /= nworkers
else:
out = np.copy(x)
return out
U.initialize()
if load_path is not None:
pi.load(load_path)
th_init = get_flat()
if MPI is not None:
MPI.COMM_WORLD.Bcast(th_init, root=0)
set_from_flat(th_init)
vfadam.sync()
print("Init param sum", th_init.sum(), flush=True)
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi, env, timesteps_per_batch, stochastic=True)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=40) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=40) # rolling buffer for episode rewards
if sum([max_iters>0, total_timesteps>0, max_episodes>0])==0:
# noththing to be done
return pi
assert sum([max_iters>0, total_timesteps>0, max_episodes>0]) < 2, \
'out of max_iters, total_timesteps, and max_episodes only one should be specified'
while True:
if callback: callback(locals(), globals())
if total_timesteps and timesteps_so_far >= total_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
logger.log("********** Iteration %i ************"%iters_so_far)
with timed("sampling"):
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, gamma, lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg["tdlamret"]
vpredbefore = seg["vpred"] # predicted value function before udpate
atarg = (atarg - atarg.mean()) / atarg.std() # standardized advantage function estimate
if hasattr(pi, "ret_rms"): pi.ret_rms.update(tdlamret)
if hasattr(pi, "ob_rms"): pi.ob_rms.update(ob) # update running mean/std for policy
args = seg["ob"], seg["ac"], atarg
fvpargs = [arr[::5] for arr in args]
def fisher_vector_product(p):
return allmean(compute_fvp(p, *fvpargs)) + cg_damping * p
assign_old_eq_new() # set old parameter values to new parameter values
with timed("computegrad"):
*lossbefore, g = compute_lossandgrad(*args)
lossbefore = allmean(np.array(lossbefore))
g = allmean(g)
if np.allclose(g, 0):
logger.log("Got zero gradient. not updating")
else:
with timed("cg"):
stepdir = cg(fisher_vector_product, g, cg_iters=cg_iters, verbose=rank==0)
assert np.isfinite(stepdir).all()
shs = .5*stepdir.dot(fisher_vector_product(stepdir))
lm = np.sqrt(shs / max_kl)
# logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g))
fullstep = stepdir / lm
expectedimprove = g.dot(fullstep)
surrbefore = lossbefore[0]
stepsize = 1.0
thbefore = get_flat()
for _ in range(10):
thnew = thbefore + fullstep * stepsize
set_from_flat(thnew)
meanlosses = surr, kl, *_ = allmean(np.array(compute_losses(*args)))
improve = surr - surrbefore
logger.log("Expected: %.3f Actual: %.3f"%(expectedimprove, improve))
if not np.isfinite(meanlosses).all():
logger.log("Got non-finite value of losses -- bad!")
elif kl > max_kl * 1.5:
logger.log("violated KL constraint. shrinking step.")
elif improve < 0:
logger.log("surrogate didn't improve. shrinking step.")
else:
logger.log("Stepsize OK!")
break
stepsize *= .5
else:
logger.log("couldn't compute a good step")
set_from_flat(thbefore)
if nworkers > 1 and iters_so_far % 20 == 0:
paramsums = MPI.COMM_WORLD.allgather((thnew.sum(), vfadam.getflat().sum())) # list of tuples
assert all(np.allclose(ps, paramsums[0]) for ps in paramsums[1:])
for (lossname, lossval) in zip(loss_names, meanlosses):
logger.record_tabular(lossname, lossval)
with timed("vf"):
for _ in range(vf_iters):
for (mbob, mbret) in dataset.iterbatches((seg["ob"], seg["tdlamret"]),
include_final_partial_batch=False, batch_size=64):
g = allmean(compute_vflossandgrad(mbob, mbret))
vfadam.update(g, vf_stepsize)
logger.record_tabular("ev_tdlam_before", explained_variance(vpredbefore, tdlamret))
lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values
if MPI is not None:
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
else:
listoflrpairs = [lrlocal]
lens, rews = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
if rank==0:
logger.dump_tabular()
return pi
def learn(policy,
env,
nsteps,
total_timesteps,
ent_coef=1e-4,
lr=1e-4,
vf_coef=0.5,
l2_coef=1e-5,
gamma=0.99,
lam=0.95,
log_interval=10,
noptepochs=4,
cliprange=0.2,
save_interval=0,
norm_adv=True,
subtract_rew_avg=False,
load_path=None,
save_path='results',
game_name='',
test_mode=False):
nenvs = env.num_envs
ob_space = env.observation_space
print("OBSPACE", ob_space)
ac_space = env.action_space
nbatch = nenvs * nsteps
nsteps_train = nsteps + nsteps // 2
pathlib.Path(save_path + '/hidden_states').mkdir(parents=True,
exist_ok=True)
model = Model(policy=policy,
ob_space=ob_space,
ac_space=ac_space,
nenv=nenvs,
nsteps=nsteps_train,
ent_coef=ent_coef,
vf_coef=vf_coef,
l2_coef=l2_coef,
cliprange=cliprange,
load_path=load_path,
test_mode=test_mode)
runner = Runner(env=env,
model=model,
nsteps=nsteps,
gamma=gamma,
lam=lam,
norm_adv=norm_adv,
subtract_rew_avg=subtract_rew_avg)
tfirststart = time.time()
nupdates = total_timesteps // (nbatch * hvd.size())
update = 0
epinfobuf = deque(maxlen=100)
while update < nupdates:
tstart = time.time()
update += 1
obs, increase_ent, advantages, masks, actions, values, neglogpacs, valids, returns, states, epinfos = runner.run(
)
with open(save_path + '/hidden_states/episode_{}.pkl'.format(update),
'wb') as f:
pickle.dump(states, f)
if hvd.size() > 1:
epinfos = flatten_lists(MPI.COMM_WORLD.allgather(epinfos))
if not test_mode:
mblossvals = []
for _ in range(noptepochs):
mblossvals.append(
model.train(lr, obs, returns, advantages, masks, actions,
values, neglogpacs, valids, increase_ent,
states))
if hvd.rank() == 0:
tnow = time.time()
tps = int(nbatch * hvd.size() / (tnow - tstart))
if update % log_interval == 0 or update == 1:
epinfobuf.extend(epinfos)
if len(epinfos) >= 100:
epinfos_to_report = epinfos
else:
epinfos_to_report = epinfobuf
ev = explained_variance(values, returns)
logger.logkv("serial_timesteps", update * nsteps)
logger.logkv("nupdates", update)
logger.logkv("total_timesteps", update * nbatch * hvd.size())
logger.logkv("tps", tps)
logger.logkv("explained_variance", float(ev))
logger.logkv(
'eprewmean',
safemean([epinfo['r'] for epinfo in epinfos_to_report]))
logger.logkv(
'eplenmean',
safemean([epinfo['l'] for epinfo in epinfos_to_report]))
if not test_mode:
lossvals = np.mean(mblossvals, axis=0)
for (lossval, lossname) in zip(lossvals, model.loss_names):
logger.logkv(lossname, lossval)
if hasattr(env, 'max_starting_point'):
logger.logkv('max_starting_point',
env.max_starting_point)
logger.logkv(
'as_good_as_demo_start',
safemean([
epinfo['as_good_as_demo']
for epinfo in epinfos_to_report
if epinfo['starting_point'] <=
env.max_starting_point
]))
logger.logkv(
'as_good_as_demo_all',
safemean([
epinfo['as_good_as_demo']
for epinfo in epinfos_to_report
]))
logger.logkv(
'perc_started_below_max_sp',
safemean([
epinfo['starting_point'] <=
env.max_starting_point
for epinfo in epinfos_to_report
]))
elif hasattr(env.venv, 'max_starting_point'):
logger.logkv('max_starting_point',
env.venv.max_starting_point)
logger.logkv(
'as_good_as_demo_start',
safemean([
epinfo['as_good_as_demo']
for epinfo in epinfos_to_report
if epinfo['starting_point'] <=
env.venv.max_starting_point
]))
logger.logkv(
'as_good_as_demo_all',
safemean([
epinfo['as_good_as_demo']
for epinfo in epinfos_to_report
]))
logger.logkv(
'perc_started_below_max_sp',
safemean([
epinfo['starting_point'] <=
env.venv.max_starting_point
for epinfo in epinfos_to_report
]))
logger.logkv('time_elapsed', tnow - tfirststart)
logger.logkv('perc_valid', np.mean(valids))
logger.logkv('tcount', update * nbatch * hvd.size())
logger.dumpkvs()
if save_interval and (update % save_interval == 0
or update == 1) and not test_mode:
savepath = osp.join(osp.join(save_path, game_name),
'%.6i' % update)
print('Saving to', savepath)
model.save(savepath)
env.close()
def learn(env, policy_func, *,
timesteps_per_batch, # what to train on
max_kl, cg_iters,
gamma, lam, # advantage estimation
entcoeff=0.0,
cg_damping=1e-2,
vf_stepsize=3e-4,
vf_iters =3,
max_timesteps=0, max_episodes=0, max_iters=0, # time constraint
callback=None
):
nworkers = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
np.set_printoptions(precision=3)
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_func("pi", ob_space, ac_space)
oldpi = policy_func("oldpi", ob_space, ac_space)
atarg = tf.placeholder(dtype=tf.float32, shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
ob = U.get_placeholder_cached(name="ob")
ac = pi.pdtype.sample_placeholder([None])
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = U.mean(kloldnew)
meanent = U.mean(ent)
entbonus = entcoeff * meanent
vferr = U.mean(tf.square(pi.vpred - ret))
ratio = tf.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac)) # advantage * pnew / pold
surrgain = U.mean(ratio * atarg)
optimgain = surrgain + entbonus
losses = [optimgain, meankl, entbonus, surrgain, meanent]
loss_names = ["optimgain", "meankl", "entloss", "surrgain", "entropy"]
dist = meankl
all_var_list = pi.get_trainable_variables()
var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("pol")]
vf_var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("vf")]
vfadam = MpiAdam(vf_var_list)
get_flat = U.GetFlat(var_list)
set_from_flat = U.SetFromFlat(var_list)
klgrads = tf.gradients(dist, var_list)
flat_tangent = tf.placeholder(dtype=tf.float32, shape=[None], name="flat_tan")
shapes = [var.get_shape().as_list() for var in var_list]
start = 0
tangents = []
for shape in shapes:
sz = U.intprod(shape)
tangents.append(tf.reshape(flat_tangent[start:start+sz], shape))
start += sz
gvp = tf.add_n([U.sum(g*tangent) for (g, tangent) in zipsame(klgrads, tangents)]) #pylint: disable=E1111
fvp = U.flatgrad(gvp, var_list)
assign_old_eq_new = U.function([],[], updates=[tf.assign(oldv, newv)
for (oldv, newv) in zipsame(oldpi.get_variables(), pi.get_variables())])
compute_losses = U.function([ob, ac, atarg], losses)
compute_lossandgrad = U.function([ob, ac, atarg], losses + [U.flatgrad(optimgain, var_list)])
compute_fvp = U.function([flat_tangent, ob, ac, atarg], fvp)
compute_vflossandgrad = U.function([ob, ret], U.flatgrad(vferr, vf_var_list))
@contextmanager
def timed(msg):
if rank == 0:
print(colorize(msg, color='magenta'))
tstart = time.time()
yield
print(colorize("done in %.3f seconds"%(time.time() - tstart), color='magenta'))
else:
yield
def allmean(x):
assert isinstance(x, np.ndarray)
out = np.empty_like(x)
MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
out /= nworkers
return out
U.initialize()
th_init = get_flat()
MPI.COMM_WORLD.Bcast(th_init, root=0)
set_from_flat(th_init)
vfadam.sync()
print("Init param sum", th_init.sum(), flush=True)
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi, env, timesteps_per_batch, stochastic=True)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=40) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=40) # rolling buffer for episode rewards
assert sum([max_iters>0, max_timesteps>0, max_episodes>0])==1
while True:
if callback: callback(locals(), globals())
if max_timesteps and timesteps_so_far >= max_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
logger.log("********** Iteration %i ************"%iters_so_far)
with timed("sampling"):
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, gamma, lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg["tdlamret"]
vpredbefore = seg["vpred"] # predicted value function before udpate
atarg = (atarg - atarg.mean()) / atarg.std() # standardized advantage function estimate
if hasattr(pi, "ret_rms"): pi.ret_rms.update(tdlamret)
if hasattr(pi, "ob_rms"): pi.ob_rms.update(ob) # update running mean/std for policy
args = seg["ob"], seg["ac"], atarg
fvpargs = [arr[::5] for arr in args]
def fisher_vector_product(p):
return allmean(compute_fvp(p, *fvpargs)) + cg_damping * p
assign_old_eq_new() # set old parameter values to new parameter values
with timed("computegrad"):
*lossbefore, g = compute_lossandgrad(*args)
lossbefore = allmean(np.array(lossbefore))
g = allmean(g)
if np.allclose(g, 0):
logger.log("Got zero gradient. not updating")
else:
with timed("cg"):
stepdir = cg(fisher_vector_product, g, cg_iters=cg_iters, verbose=rank==0)
assert np.isfinite(stepdir).all()
shs = .5*stepdir.dot(fisher_vector_product(stepdir))
lm = np.sqrt(shs / max_kl)
# logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g))
fullstep = stepdir / lm
expectedimprove = g.dot(fullstep)
surrbefore = lossbefore[0]
stepsize = 1.0
thbefore = get_flat()
for _ in range(10):
thnew = thbefore + fullstep * stepsize
set_from_flat(thnew)
meanlosses = surr, kl, *_ = allmean(np.array(compute_losses(*args)))
improve = surr - surrbefore
logger.log("Expected: %.3f Actual: %.3f"%(expectedimprove, improve))
if not np.isfinite(meanlosses).all():
logger.log("Got non-finite value of losses -- bad!")
elif kl > max_kl * 1.5:
logger.log("violated KL constraint. shrinking step.")
elif improve < 0:
logger.log("surrogate didn't improve. shrinking step.")
else:
logger.log("Stepsize OK!")
break
stepsize *= .5
else:
logger.log("couldn't compute a good step")
set_from_flat(thbefore)
if nworkers > 1 and iters_so_far % 20 == 0:
paramsums = MPI.COMM_WORLD.allgather((thnew.sum(), vfadam.getflat().sum())) # list of tuples
assert all(np.allclose(ps, paramsums[0]) for ps in paramsums[1:])
for (lossname, lossval) in zip(loss_names, meanlosses):
logger.record_tabular(lossname, lossval)
with timed("vf"):
for _ in range(vf_iters):
for (mbob, mbret) in dataset.iterbatches((seg["ob"], seg["tdlamret"]),
include_final_partial_batch=False, batch_size=64):
g = allmean(compute_vflossandgrad(mbob, mbret))
vfadam.update(g, vf_stepsize)
logger.record_tabular("ev_tdlam_before", explained_variance(vpredbefore, tdlamret))
lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
if rank==0:
logger.dump_tabular()
def learn(
env,
policy_fn,
*,
timesteps_per_batch, # what to train on
epsilon,
beta,
cg_iters,
gamma,
lam, # advantage estimation
trial,
sess,
method,
entcoeff=0.0,
cg_damping=1e-2,
kl_target=0.01,
crosskl_coeff=0.01,
vf_stepsize=3e-4,
vf_iters=3,
max_timesteps=0,
max_episodes=0,
max_iters=0, # time constraint
callback=None,
TRPO=False):
nworkers = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
np.set_printoptions(precision=3)
# Setup losses and stuff
# ----------------------------------------
total_space = env.total_space
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_fn("pi", ob_space, ac_space, ob_name="ob")
oldpi = policy_fn("oldpi", ob_space, ac_space, ob_name="ob")
gpi = policy_fn("gpi", total_space, ac_space, ob_name="gob")
goldpi = policy_fn("goldpi", total_space, ac_space, ob_name="gob")
atarg = tf.placeholder(
dtype=tf.float32,
shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
gatarg = tf.placeholder(dtype=tf.float32, shape=[None])
gret = tf.placeholder(dtype=tf.float32, shape=[None])
ob = U.get_placeholder_cached(name="ob")
gob = U.get_placeholder_cached(name='gob')
ac = pi.pdtype.sample_placeholder([None])
crosskl_c = tf.placeholder(dtype=tf.float32, shape=[])
# crosskl_c = 0.01
kloldnew = oldpi.pd.kl(pi.pd)
gkloldnew = goldpi.pd.kl(gpi.pd)
#TODO: check if it can work in this way
# crosskl_ob = pi.pd.kl(goldpi.pd)
# crosskl_gob = gpi.pd.kl(oldpi.pd)
crosskl_gob = pi.pd.kl(gpi.pd)
crosskl_ob = gpi.pd.kl(pi.pd)
# crosskl
pdmean = pi.pd.mean
pdstd = pi.pd.std
gpdmean = gpi.pd.mean
gpdstd = gpi.pd.std
ent = pi.pd.entropy()
gent = gpi.pd.entropy()
old_entropy = oldpi.pd.entropy()
gold_entropy = goldpi.pd.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
meancrosskl = tf.reduce_mean(crosskl_ob)
# meancrosskl = tf.maximum(tf.reduce_mean(crosskl_ob - 100), 0)
gmeankl = tf.reduce_mean(gkloldnew)
gmeanent = tf.reduce_mean(gent)
gmeancrosskl = tf.reduce_mean(crosskl_gob)
vferr = tf.reduce_mean(tf.square(pi.vpred - ret))
gvferr = tf.reduce_mean(tf.square(gpi.vpred - gret))
ratio = tf.exp(pi.pd.logp(ac) -
oldpi.pd.logp(ac)) # advantage * pnew / pold
gratio = tf.exp(gpi.pd.logp(ac) - goldpi.pd.logp(ac))
# Ratio objective
# surrgain = tf.reduce_mean(ratio * atarg)
# gsurrgain = tf.reduce_mean(gratio * gatarg)
# Log objective
surrgain = tf.reduce_mean(pi.pd.logp(ac) * atarg)
gsurrgain = tf.reduce_mean(gpi.pd.logp(ac) * gatarg)
# optimgain = surrgain + crosskl_c * meancrosskl
optimgain = surrgain
losses = [
optimgain, meankl, meancrosskl, surrgain, meanent,
tf.reduce_mean(ratio)
]
loss_names = [
"optimgain", "meankl", "meancrosskl", "surrgain", "entropy", "ratio"
]
# goptimgain = gsurrgain + crosskl_c * gmeancrosskl
goptimgain = gsurrgain
glosses = [
goptimgain, gmeankl, gmeancrosskl, gsurrgain, gmeanent,
tf.reduce_mean(gratio)
]
gloss_names = [
"goptimgain", "gmeankl", "gmeancrosskl", "gsurrgain", "gentropy",
"gratio"
]
dist = meankl
gdist = gmeankl
all_pi_var_list = pi.get_trainable_variables()
all_var_list = [
v for v in all_pi_var_list if v.name.split("/")[0].startswith("pi")
]
var_list = [
v for v in all_var_list if v.name.split("/")[1].startswith("pol")
]
vf_var_list = [
v for v in all_var_list if v.name.split("/")[1].startswith("vf")
]
vfadam = MpiAdam(vf_var_list)
poladam = MpiAdam(var_list)
gall_gpi_var_list = gpi.get_trainable_variables()
gall_var_list = [
v for v in gall_gpi_var_list if v.name.split("/")[0].startswith("gpi")
]
gvar_list = [
v for v in gall_var_list if v.name.split("/")[1].startswith("pol")
]
gvf_var_list = [
v for v in gall_var_list if v.name.split("/")[1].startswith("vf")
]
gvfadam = MpiAdam(gvf_var_list)
# gpoladpam = MpiAdam(gvar_list)
get_flat = U.GetFlat(var_list)
set_from_flat = U.SetFromFlat(var_list)
klgrads = tf.gradients(dist, var_list)
# crossklgrads = tf.gradients(meancrosskl, var_list)
flat_tangent = tf.placeholder(dtype=tf.float32,
shape=[None],
name="flat_tan")
shapes = [var.get_shape().as_list() for var in var_list]
start = 0
tangents = []
for shape in shapes:
sz = U.intprod(shape)
tangents.append(tf.reshape(flat_tangent[start:start + sz], shape))
start += sz
gvp = tf.add_n([
tf.reduce_sum(g * tangent)
for (g, tangent) in zipsame(klgrads, tangents)
]) #pylint: disable=E1111
fvp = U.flatgrad(gvp, var_list)
gget_flat = U.GetFlat(gvar_list)
gset_from_flat = U.SetFromFlat(gvar_list)
gklgrads = tf.gradients(gdist, gvar_list)
# gcrossklgrads = tf.gradients(gmeancrosskl, gvar_list)
gflat_tangent = tf.placeholder(dtype=tf.float32,
shape=[None],
name="gflat_tan")
gshapes = [var.get_shape().as_list() for var in gvar_list]
gstart = 0
gtangents = []
for shape in gshapes:
sz = U.intprod(shape)
gtangents.append(tf.reshape(gflat_tangent[gstart:gstart + sz], shape))
gstart += sz
ggvp = tf.add_n([
tf.reduce_sum(g * tangent)
for (g, tangent) in zipsame(gklgrads, gtangents)
]) #pylint: disable=E1111
gfvp = U.flatgrad(ggvp, gvar_list)
assign_old_eq_new = U.function(
[], [],
updates=[
tf.assign(oldv, newv)
for (oldv,
newv) in zipsame(oldpi.get_variables(), pi.get_variables())
])
gassign_old_eq_new = U.function(
[], [],
updates=[
tf.assign(oldv, newv)
for (oldv,
newv) in zipsame(goldpi.get_variables(), gpi.get_variables())
])
compute_losses = U.function([crosskl_c, gob, ob, ac, atarg], losses)
compute_lossandgrad = U.function([crosskl_c, gob, ob, ac, atarg], losses +
[U.flatgrad(optimgain, var_list)])
compute_fvp = U.function([flat_tangent, ob, ac, atarg], fvp)
compute_vflossandgrad = U.function([ob, ret],
U.flatgrad(vferr, vf_var_list))
compute_crossklandgrad = U.function([ob, gob],
U.flatgrad(meancrosskl, var_list))
gcompute_losses = U.function([crosskl_c, ob, gob, ac, gatarg], glosses)
gcompute_lossandgrad = U.function([crosskl_c, ob, gob, ac, gatarg],
glosses +
[U.flatgrad(goptimgain, gvar_list)])
gcompute_fvp = U.function([gflat_tangent, gob, ac, gatarg], gfvp)
gcompute_vflossandgrad = U.function([gob, gret],
U.flatgrad(gvferr, gvf_var_list))
# compute_gcrossklandgrad = U.function([gob, ob], U.flatgrad(gmeancrosskl, gvar_list))
saver = tf.train.Saver()
@contextmanager
def timed(msg):
if rank == 0:
print(colorize(msg, color='magenta'))
tstart = time.time()
yield
print(
colorize("done in %.3f seconds" % (time.time() - tstart),
color='magenta'))
else:
yield
def allmean(x):
assert isinstance(x, np.ndarray)
out = np.empty_like(x)
MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
out /= nworkers
return out
U.initialize()
guided_initilizer(gpol=gvar_list,
gvf=gvf_var_list,
fpol=var_list,
fvf=vf_var_list)
th_init = get_flat()
MPI.COMM_WORLD.Bcast(th_init, root=0)
set_from_flat(th_init)
vfadam.sync()
poladam.sync()
print("Init final policy param sum", th_init.sum(), flush=True)
gth_init = gget_flat()
MPI.COMM_WORLD.Bcast(gth_init, root=0)
gset_from_flat(gth_init)
gvfadam.sync()
# gpoladpam.sync()
print("Init guided policy param sum", gth_init.sum(), flush=True)
# Initialize eta, omega optimizer
init_eta = 0.5
init_omega = 2.0
eta_omega_optimizer = EtaOmegaOptimizer(beta, epsilon, init_eta,
init_omega)
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi,
gpi,
env,
timesteps_per_batch,
stochastic=True)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
num_iters = max_timesteps // timesteps_per_batch
lenbuffer = deque(maxlen=40) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=40) # rolling buffer for episode rewards
assert sum([max_iters > 0, max_timesteps > 0, max_episodes > 0]) == 1
while True:
if callback: callback(locals(), globals())
if max_timesteps and timesteps_so_far >= max_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
logger.log("********** Iteration %i ************" % iters_so_far)
with timed("sampling"):
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, gamma, lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg[
"tdlamret"]
gob, gatarg, gtdlamret = seg["gob"], seg["gadv"], seg["gtdlamret"]
vpredbefore = seg["vpred"] # predicted value function before udpate
gvpredbefore = seg["gvpred"]
atarg = (atarg - atarg.mean()
) / atarg.std() # standardized advantage function estimate
gatarg = (gatarg - gatarg.mean()) / gatarg.std()
if hasattr(pi, "ret_rms"): pi.ret_rms.update(tdlamret)
if hasattr(pi, "ob_rms"):
pi.ob_rms.update(ob) # update running mean/std for policy
if hasattr(gpi, "ret_rms"): gpi.ret_rms.update(gtdlamret)
if hasattr(gpi, "ob_rms"): gpi.ob_rms.update(gob)
args = crosskl_coeff, seg["gob"], seg["ob"], seg["ac"], atarg
fvpargs = [arr[::5] for arr in args[2:]]
gargs = crosskl_coeff, seg["ob"], seg["gob"], seg["ac"], gatarg
gfvpargs = [arr[::5] for arr in gargs[2:]]
def fisher_vector_product(p):
return allmean(compute_fvp(p, *fvpargs)) + cg_damping * p
def gfisher_vector_product(p):
return allmean(gcompute_fvp(p, *gfvpargs)) + cg_damping * p
assign_old_eq_new() # set old parameter values to new parameter values
gassign_old_eq_new()
with timed("computegrad"):
*lossbefore, g = compute_lossandgrad(*args)
*glossbefore, gg = gcompute_lossandgrad(*gargs)
lossbefore = allmean(np.array(lossbefore))
g = allmean(g)
glossbefore = allmean(np.array(glossbefore))
gg = allmean(gg)
if np.allclose(g, 0) or np.allclose(gg, 0):
logger.log("Got zero gradient. not updating")
else:
with timed("cg"):
stepdir = cg(fisher_vector_product,
g,
cg_iters=cg_iters,
verbose=rank == 0)
gstepdir = cg(gfisher_vector_product,
gg,
cg_iters=cg_iters,
verbose=rank == 0)
assert np.isfinite(gstepdir).all()
assert np.isfinite(stepdir).all()
if TRPO:
#
# TRPO specific code.
# Find correct step size using line search
#
#TODO: also enable guided learning for TRPO
shs = .5 * stepdir.dot(fisher_vector_product(stepdir))
lm = np.sqrt(shs / epsilon)
# logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g))
fullstep = stepdir / lm
expectedimprove = g.dot(fullstep)
surrbefore = lossbefore[0]
stepsize = 1.0
thbefore = get_flat()
for _ in range(10):
thnew = thbefore + fullstep * stepsize
set_from_flat(thnew)
meanlosses = surr, kl, *_ = allmean(
np.array(compute_losses(*args)))
improve = surr - surrbefore
logger.log("Expected: %.3f Actual: %.3f" %
(expectedimprove, improve))
if not np.isfinite(meanlosses).all():
logger.log("Got non-finite value of losses -- bad!")
elif kl > epsilon * 1.5:
logger.log("violated KL constraint. shrinking step.")
elif improve < 0:
logger.log("surrogate didn't improve. shrinking step.")
else:
logger.log("Stepsize OK!")
break
stepsize *= .5
else:
logger.log("couldn't compute a good step")
set_from_flat(thbefore)
else:
#
# COPOS specific implementation.
#
copos_update_dir = stepdir
gcopos_update_dir = gstepdir
# Split direction into log-linear 'w_theta' and non-linear 'w_beta' parts
w_theta, w_beta = pi.split_w(copos_update_dir)
gw_theta, gw_beta = gpi.split_w(gcopos_update_dir)
# q_beta(s,a) = \grad_beta \log \pi(a|s) * w_beta
# = features_beta(s) * K^T * Prec * a
# q_beta = self.target.get_q_beta(features_beta, actions)
Waa, Wsa = pi.w2W(w_theta)
wa = pi.get_wa(ob, w_beta)
gWaa, gWsa = gpi.w2W(gw_theta)
gwa = gpi.get_wa(gob, gw_beta)
varphis = pi.get_varphis(ob)
gvarphis = gpi.get_varphis(gob)
# Optimize eta and omega
tmp_ob = np.zeros(
(1, ) + ob_space.shape
) # We assume that entropy does not depend on the NN
old_ent = old_entropy.eval({oldpi.ob: tmp_ob})[0]
eta, omega = eta_omega_optimizer.optimize(
w_theta, Waa, Wsa, wa, varphis, pi.get_kt(),
pi.get_prec_matrix(), pi.is_new_policy_valid, old_ent)
logger.log("Initial eta of final policy: " + str(eta) +
" and omega: " + str(omega))
gtmp_ob = np.zeros((1, ) + total_space.shape)
gold_ent = gold_entropy.eval({goldpi.ob: gtmp_ob})[0]
geta, gomega = eta_omega_optimizer.optimize(
gw_theta, gWaa, gWsa, gwa, gvarphis, gpi.get_kt(),
gpi.get_prec_matrix(), gpi.is_new_policy_valid, gold_ent)
logger.log("Initial eta of guided policy: " + str(geta) +
" and omega: " + str(gomega))
current_theta_beta = get_flat()
prev_theta, prev_beta = pi.all_to_theta_beta(
current_theta_beta)
gcurrent_theta_beta = gget_flat()
gprev_theta, gprev_beta = gpi.all_to_theta_beta(
gcurrent_theta_beta)
for i in range(2):
# Do a line search for both theta and beta parameters by adjusting only eta
eta = eta_search(w_theta, w_beta, eta, omega, allmean,
compute_losses, get_flat, set_from_flat,
pi, epsilon, args)
logger.log("Updated eta of final policy, eta: " +
str(eta) + " and omega: " + str(omega))
# Find proper omega for new eta. Use old policy parameters first.
set_from_flat(pi.theta_beta_to_all(prev_theta, prev_beta))
eta, omega = \
eta_omega_optimizer.optimize(w_theta, Waa, Wsa, wa, varphis, pi.get_kt(),
pi.get_prec_matrix(), pi.is_new_policy_valid, old_ent, eta)
logger.log("Updated omega of final policy, eta: " +
str(eta) + " and omega: " + str(omega))
geta = eta_search(gw_theta, gw_beta, geta, gomega, allmean,
gcompute_losses, gget_flat,
gset_from_flat, gpi, epsilon, gargs)
logger.log("updated eta of guided policy, eta:" +
str(geta) + "and omega:" + str(gomega))
gset_from_flat(
gpi.theta_beta_to_all(gprev_theta, gprev_beta))
geta, gomega = eta_omega_optimizer.optimize(
gw_theta, gWaa, gWsa, gwa, gvarphis, gpi.get_kt(),
gpi.get_prec_matrix(), gpi.is_new_policy_valid,
gold_ent, geta)
logger.log("Updated omega of guided policy, eta:" +
str(geta) + "and omega:" + str(gomega))
# Use final policy
logger.log("Final eta of final policy: " + str(eta) +
" and omega: " + str(omega))
logger.log("Final eta of guided policy: " + str(geta) +
"and omega:" + str(gomega))
cur_theta = (eta * prev_theta +
w_theta.reshape(-1, )) / (eta + omega)
cur_beta = prev_beta + w_beta.reshape(-1, ) / eta
set_from_flat(pi.theta_beta_to_all(cur_theta, cur_beta))
gcur_theta = (geta * gprev_theta +
gw_theta.reshape(-1, )) / (geta + gomega)
gcur_beta = gprev_beta + gw_beta.reshape(-1, ) / geta
gset_from_flat(gpi.theta_beta_to_all(gcur_theta, gcur_beta))
meanlosses = surr, kl, crosskl, *_ = allmean(
np.array(compute_losses(*args)))
gmeanlosses = gsurr, gkl, gcrosskl, *_ = allmean(
np.array(gcompute_losses(*gargs)))
# poladam.update(allmean(compute_crossklandgrad(ob, gob)), vf_stepsize)
# gpoladpam.update(allmean(compute_gcrossklandgrad(gob, ob)), vf_stepsize)
for _ in range(vf_iters):
for (mbob, mbgob) in dataset.iterbatches(
(seg["ob"], seg["gob"]),
include_final_partial_batch=False,
batch_size=64):
g = allmean(compute_crossklandgrad(mbob, mbgob))
poladam.update(g, vf_stepsize)
# pd_crosskl = np.mean((crosskl, gcrosskl))
# pd_crosskl = crosskl
# if pd_crosskl < kl_target / 2:
# print("KL divergence between guided policy and final control policy is small, reduce the coefficient")
# crosskl_coeff /= 1.5
# elif pd_crosskl > kl_target * 2:
# print("KL divergence between guided policy and final control policy is large, increse the coefficient")
# crosskl_coeff *= 1.5
# crosskl_coeff = np.clip(crosskl_coeff, 1e-4, 30)
# if nworkers > 1 and iters_so_far % 20 == 0:
# paramsums = MPI.COMM_WORLD.allgather((thnew.sum(), vfadam.getflat().sum())) # list of tuples
# assert all(np.allclose(ps, paramsums[0]) for ps in paramsums[1:])
for (lossname, lossval) in zip(loss_names, meanlosses):
logger.record_tabular(lossname, lossval)
for (lossname, lossval) in zip(gloss_names, gmeanlosses):
logger.record_tabular(lossname, lossval)
with timed("vf"):
for _ in range(vf_iters):
for (mbob, mbret) in dataset.iterbatches(
(seg["ob"], seg["tdlamret"]),
include_final_partial_batch=False,
batch_size=64):
g = allmean(compute_vflossandgrad(mbob, mbret))
vfadam.update(g, vf_stepsize)
for (mbob, mbret) in dataset.iterbatches(
(seg["gob"], seg["gtdlamret"]),
include_final_partial_batch=False,
batch_size=64):
gg = allmean(gcompute_vflossandgrad(mbob, mbret))
gvfadam.update(gg, vf_stepsize)
logger.record_tabular("ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
logger.record_tabular("gev_tdlam_before",
explained_variance(gvpredbefore, gtdlamret))
lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
logger.record_tabular("CrossKLCoeff :", crosskl_coeff)
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
logger.record_tabular("Name", method)
logger.record_tabular("Iteration", iters_so_far)
logger.record_tabular("trial", trial)
if rank == 0:
logger.dump_tabular()
if iters_so_far % 100 == 0 or iters_so_far == 1 or iters_so_far == num_iters:
# sess = tf.get_default_session()
checkdir = get_dir(osp.join(logger.get_dir(), 'checkpoints'))
savepath = osp.join(checkdir, '%.5i.ckpt' % iters_so_far)
saver.save(sess, save_path=savepath)
print("save model to path:", savepath)
def learn(
network,
env,
seed=None,
nsteps=5,
total_timesteps=int(80e6),
vf_coef=0.5,
ent_coef=0.01,
max_grad_norm=0.5,
lr=7e-4,
lrschedule='linear',
epsilon=1e-5,
alpha=0.99,
gamma=0.99,
log_interval=100,
load_path=None,
nm_customization_args={
'use_extended_write_op': False,
'log_model_parameters': False,
'optimizer': 'RMSProp'
},
**network_kwargs):
'''
Main entrypoint for A2C algorithm. Train a policy with given network architecture on a given environment using a2c algorithm.
Parameters:
-----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See baselines.common/policies.py/lstm for more details on using recurrent nets in policies
env: RL environment. Should implement interface similar to VecEnv (baselines.common/vec_env) or be wrapped with DummyVecEnv (baselines.common/vec_env/dummy_vec_env.py)
seed: seed to make random number sequence in the alorightm reproducible. By default is None which means seed from system noise generator (not reproducible)
nsteps: int, number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel)
total_timesteps: int, total number of timesteps to train on (default: 80M)
vf_coef: float, coefficient in front of value function loss in the total loss function (default: 0.5)
ent_coef: float, coeffictiant in front of the policy entropy in the total loss function (default: 0.01)
max_gradient_norm: float, gradient is clipped to have global L2 norm no more than this value (default: 0.5)
lr: float, learning rate for RMSProp (current implementation has RMSProp hardcoded in) (default: 7e-4)
lrschedule: schedule of learning rate. Can be 'linear', 'constant', or a function [0..1] -> [0..1] that takes fraction of the training progress as input and
returns fraction of the learning rate (specified as lr) as output
epsilon: float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5)
alpha: float, RMSProp decay parameter (default: 0.99)
gamma: float, reward discounting parameter (default: 0.99)
log_interval: int, specifies how frequently the logs are printed out (default: 100)
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
'''
set_global_seeds(seed)
# Get the nb of env
nenvs = env.num_envs
policy = build_policy(env, network, **network_kwargs)
# Instantiate the model object (that creates step_model and train_model)
model = Model(policy=policy,
env=env,
nsteps=nsteps,
ent_coef=ent_coef,
vf_coef=vf_coef,
max_grad_norm=max_grad_norm,
lr=lr,
alpha=alpha,
epsilon=epsilon,
total_timesteps=total_timesteps,
lrschedule=lrschedule,
optimizer=nm_customization_args['optimizer'])
if load_path is not None:
model.load(load_path)
if nm_customization_args['log_model_parameters']:
tf.contrib.slim.model_analyzer.analyze_vars(tf.trainable_variables(),
print_info=True)
#for var in tf.trainable_variables():
# tf.summary.histogram(var.name[:-2], var)
writer = tf.summary.FileWriter(nm_customization_args['log_path'],
graph=model.sess.graph)
#summary_op = tf.summary.merge_all()
# Instantiate the runner object
runner = Runner(
env,
model,
nsteps=nsteps,
gamma=gamma,
use_extended_write_op=nm_customization_args['use_extended_write_op'],
max_positions=nm_customization_args['max_positions'])
epinfobuf = deque(maxlen=100)
# Calculate the batch_size
nbatch = nenvs * nsteps
# Start total timer
tstart = time.time()
for update in range(1, total_timesteps // nbatch + 1):
# Get mini batch of experiences
obs, states, rewards, masks, actions, values, pos, epinfos = runner.run(
update)
epinfobuf.extend(epinfos)
policy_loss, value_loss, policy_entropy = model.train(
obs, states, rewards, masks, actions, values)
nseconds = time.time() - tstart
# Calculate the fps (frame per second)
fps = int((update * nbatch) / nseconds)
if update % log_interval == 0 or update == 1:
# Calculates if value function is a good predicator of the returns (ev > 1)
# or if it's just worse than predicting nothing (ev =< 0)
ev = explained_variance(values, rewards)
logger.record_tabular("nupdates", update)
logger.record_tabular("total_timesteps", update * nbatch)
logger.record_tabular("fps", fps)
logger.record_tabular("policy_entropy", float(policy_entropy))
logger.record_tabular("value_loss", float(value_loss))
logger.record_tabular("explained_variance", float(ev))
logger.record_tabular(
"eprewmean", safemean([epinfo['r'] for epinfo in epinfobuf]))
logger.record_tabular(
"eplenmean", safemean([epinfo['l'] for epinfo in epinfobuf]))
logger.dump_tabular()
#if nm_customization_args['log_model_parameters']:
# writer.add_summary(model.sess.run(summary_op))
if nm_customization_args['log_model_parameters']:
writer.flush()
writer.close()
return model
def update(self, obs, actions, atarg, returns, vpredbefore, nb):
obs = tf.constant(obs)
actions = tf.constant(actions)
atarg = tf.constant(atarg)
returns = tf.constant(returns)
estimates = tf.constant(self.estimates[nb])
multipliers = tf.constant(self.multipliers[nb])
args = obs, actions, atarg, estimates, multipliers
# Sampling every 5
fvpargs = [arr[::1] for arr in (obs, actions)]
hvp = lambda p: self.allmean(self.compute_hvp(p, *fvpargs).numpy()) + self.cg_damping * p
jjvp = lambda p: self.allmean(self.compute_jjvp(self.reshape_from_flat(p), *fvpargs).numpy()) + self.cg_damping * p
fvp = lambda p: self.allmean(self.my_compute_fvp(self.reshape_from_flat(p), *fvpargs).numpy()) + self.cg_damping * p
self.assign_new_eq_old() # set old parameter values to new parameter values
# with self.timed("computegrad"):
lossbefore = self.compute_losses(*args, nb)
g = self.compute_vjp(*args, nb)
lossbefore = self.allmean(np.array(lossbefore))
g = g.numpy()
g = self.allmean(g)
# # check
# v1 = jjvp(g)
# v2 = fvp(g)
# print(v1)
# print(v2)
# input()
if np.allclose(g, 0):
logger.log("Got zero gradient. not updating")
else:
# with self.timed("cg"):
stepdir = cg(fvp, g, cg_iters=self.cg_iters, verbose=self.rank==0)
assert np.isfinite(stepdir).all()
shs = .5*stepdir.dot(fvp(stepdir))
lm = np.sqrt(shs / self.max_kl)
logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g))
fullstep = stepdir / lm
expectedimprove = g.dot(fullstep)
lagrangebefore, surrbefore, syncbefore, *_ = lossbefore
stepsize = 1.0
thbefore = self.get_flat()
for _ in range(10):
thnew = thbefore + fullstep * stepsize
self.set_from_flat(thnew)
meanlosses = lagrange, surr, syncloss, kl, *_ = self.allmean(np.array(self.compute_losses(*args, nb)))
improve = lagrangebefore - lagrange
performance_improve = surr - surrbefore
sync_improve = syncbefore - syncloss
print(lagrangebefore, surrbefore, syncbefore)
print(lagrange, surr, syncloss)
# input()
logger.log("Expected: %.3f Actual: %.3f"%(expectedimprove, improve))
if not np.isfinite(meanlosses).all():
logger.log("Got non-finite value of losses -- bad!")
elif kl > self.max_kl * 1.5:
logger.log("violated KL constraint. shrinking step.")
elif improve < 0:
logger.log("surrogate didn't improve. shrinking step.")
else:
logger.log("Stepsize OK!")
break
stepsize *= .5
else:
logger.log("couldn't compute a good step")
self.set_from_flat(thbefore)
# with self.timed("vf"):
for _ in range(self.vf_iters):
for (mbob, mbret) in dataset.iterbatches((obs, returns),
include_final_partial_batch=False, batch_size=64):
vg = self.allmean(self.compute_vflossandgrad(mbob, mbret).numpy())
self.vfadam.update(vg, self.vf_stepsize)
logger.record_tabular("ev_tdlam_before", explained_variance(vpredbefore, returns))
def learn(
env,
policy_func,
*,
timesteps_per_batch, # timesteps per actor per update
clip_param,
entcoeff, # clipping parameter epsilon, entropy coeff
optim_epochs,
optim_stepsize,
optim_batchsize, # optimization hypers
gamma,
lam, # advantage estimation
max_timesteps=0,
max_episodes=0,
max_iters=0,
max_seconds=0, # time constraint
callback=None, # you can do anything in the callback, since it takes locals(), globals()
adam_epsilon=1e-5,
schedule='constant', # annealing for stepsize parameters (epsilon and adam)
sym_loss_weight=0.0,
return_threshold=None, # termiante learning if reaches return_threshold
op_after_init=None,
init_policy_params=None,
policy_scope=None,
max_threshold=None,
positive_rew_enforce=False,
reward_drop_bound=None,
min_iters=0,
ref_policy_params=None,
rollout_length_thershold=None):
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
if policy_scope is None:
pi = policy_func("pi", ob_space,
ac_space) # Construct network for new policy
oldpi = policy_func("oldpi", ob_space,
ac_space) # Network for old policy
else:
pi = policy_func(policy_scope, ob_space,
ac_space) # Construct network for new policy
oldpi = policy_func("old" + policy_scope, ob_space,
ac_space) # Network for old policy
atarg = tf.placeholder(
dtype=tf.float32,
shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
lrmult = tf.placeholder(
name='lrmult', dtype=tf.float32,
shape=[]) # learning rate multiplier, updated with schedule
clip_param = clip_param * lrmult # Annealed cliping parameter epislon
ob = U.get_placeholder_cached(name="ob")
ac = pi.pdtype.sample_placeholder([None])
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = U.mean(kloldnew)
meanent = U.mean(ent)
pol_entpen = (-entcoeff) * meanent
sym_loss = sym_loss_weight * U.mean(
tf.square(pi.mean - pi.mirrored_mean)) # mirror symmetric loss
ratio = tf.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac)) # pnew / pold
surr1 = ratio * atarg # surrogate from conservative policy iteration
surr2 = U.clip(ratio, 1.0 - clip_param, 1.0 + clip_param) * atarg #
pol_surr = -U.mean(tf.minimum(
surr1, surr2)) + sym_loss # PPO's pessimistic surrogate (L^CLIP)
vf_loss = U.mean(tf.square(pi.vpred - ret))
total_loss = pol_surr + pol_entpen + vf_loss
losses = [pol_surr, pol_entpen, vf_loss, meankl, meanent, sym_loss]
loss_names = ["pol_surr", "pol_entpen", "vf_loss", "kl", "ent", "sym_loss"]
var_list = pi.get_trainable_variables()
lossandgrad = U.function([ob, ac, atarg, ret, lrmult],
losses + [U.flatgrad(total_loss, var_list)])
adam = MpiAdam(var_list, epsilon=adam_epsilon)
assign_old_eq_new = U.function(
[], [],
updates=[
tf.assign(oldv, newv)
for (oldv,
newv) in zipsame(oldpi.get_variables(), pi.get_variables())
])
compute_losses = U.function([ob, ac, atarg, ret, lrmult], losses)
U.initialize()
if init_policy_params is not None:
cur_scope = pi.get_variables()[0].name[0:pi.get_variables()[0].name.
find('/')]
orig_scope = list(init_policy_params.keys()
)[0][0:list(init_policy_params.keys())[0].find('/')]
for i in range(len(pi.get_variables())):
assign_op = pi.get_variables()[i].assign(
init_policy_params[pi.get_variables()[i].name.replace(
cur_scope, orig_scope, 1)])
U.get_session().run(assign_op)
assign_op = oldpi.get_variables()[i].assign(
init_policy_params[pi.get_variables()[i].name.replace(
cur_scope, orig_scope, 1)])
U.get_session().run(assign_op)
if ref_policy_params is not None:
ref_pi = policy_func("ref_pi", ob_space, ac_space)
cur_scope = ref_pi.get_variables()[0].name[0:ref_pi.get_variables()[0].
name.find('/')]
orig_scope = list(ref_policy_params.keys()
)[0][0:list(ref_policy_params.keys())[0].find('/')]
for i in range(len(ref_pi.get_variables())):
assign_op = ref_pi.get_variables()[i].assign(
ref_policy_params[ref_pi.get_variables()[i].name.replace(
cur_scope, orig_scope, 1)])
U.get_session().run(assign_op)
env.env.env.ref_policy = ref_pi
adam.sync()
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi,
env,
timesteps_per_batch,
stochastic=True)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=100) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=100) # rolling buffer for episode rewards
assert sum(
[max_iters > 0, max_timesteps > 0, max_episodes > 0,
max_seconds > 0]) == 1, "Only one time constraint permitted"
max_thres_satisfied = max_threshold is None
adjust_ratio = 0.0
prev_avg_rew = -1000000
revert_parameters = {}
variables = pi.get_variables()
for i in range(len(variables)):
cur_val = variables[i].eval()
revert_parameters[variables[i].name] = cur_val
revert_data = [0, 0, 0]
while True:
if callback: callback(locals(), globals())
if max_timesteps and timesteps_so_far >= max_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
elif max_seconds and time.time() - tstart >= max_seconds:
break
if schedule == 'constant':
cur_lrmult = 1.0
elif schedule == 'linear':
cur_lrmult = max(1.0 - float(timesteps_so_far) / max_timesteps, 0)
else:
raise NotImplementedError
logger.log("********** Iteration %i ************" % iters_so_far)
seg = seg_gen.__next__()
if reward_drop_bound is not None:
lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
revert_iteration = False
if np.mean(
rewbuffer
) < prev_avg_rew - 50: # detect significant drop in performance, revert to previous iteration
print("Revert Iteration!!!!!")
revert_iteration = True
else:
prev_avg_rew = np.mean(rewbuffer)
logger.record_tabular("Revert Rew", prev_avg_rew)
if revert_iteration: # revert iteration
for i in range(len(pi.get_variables())):
assign_op = pi.get_variables()[i].assign(
revert_parameters[pi.get_variables()[i].name])
U.get_session().run(assign_op)
episodes_so_far = revert_data[0]
timesteps_so_far = revert_data[1]
iters_so_far = revert_data[2]
continue
else:
variables = pi.get_variables()
for i in range(len(variables)):
cur_val = variables[i].eval()
revert_parameters[variables[i].name] = np.copy(cur_val)
revert_data[0] = episodes_so_far
revert_data[1] = timesteps_so_far
revert_data[2] = iters_so_far
if positive_rew_enforce:
rewlocal = (seg["pos_rews"], seg["neg_pens"], seg["rew"]
) # local values
listofrews = MPI.COMM_WORLD.allgather(rewlocal) # list of tuples
pos_rews, neg_pens, rews = map(flatten_lists, zip(*listofrews))
if np.mean(rews) < 0.0:
#min_id = np.argmin(rews)
#adjust_ratio = pos_rews[min_id]/np.abs(neg_pens[min_id])
adjust_ratio = np.max([
adjust_ratio,
np.mean(pos_rews) / np.abs(np.mean(neg_pens))
])
for i in range(len(seg["rew"])):
if np.abs(seg["rew"][i] - seg["pos_rews"][i] -
seg["neg_pens"][i]) > 1e-5:
print(seg["rew"][i], seg["pos_rews"][i],
seg["neg_pens"][i])
print('Reward wrong!')
abc
seg["rew"][i] = seg["pos_rews"][
i] + seg["neg_pens"][i] * adjust_ratio
add_vtarg_and_adv(seg, gamma, lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg[
"tdlamret"]
vpredbefore = seg["vpred"] # predicted value function before udpate
atarg = (atarg - atarg.mean()
) / atarg.std() # standardized advantage function estimate
d = Dataset(dict(ob=ob, ac=ac, atarg=atarg, vtarg=tdlamret),
shuffle=not pi.recurrent)
optim_batchsize = optim_batchsize or ob.shape[0]
if hasattr(pi, "ob_rms"):
pi.ob_rms.update(ob) # update running mean/std for policy
assign_old_eq_new() # set old parameter values to new parameter values
logger.log("Optimizing...")
logger.log(fmt_row(13, loss_names))
# Here we do a bunch of optimization epochs over the data
for _ in range(optim_epochs):
losses = [
] # list of tuples, each of which gives the loss for a minibatch
for batch in d.iterate_once(optim_batchsize):
*newlosses, g = lossandgrad(batch["ob"], batch["ac"],
batch["atarg"], batch["vtarg"],
cur_lrmult)
adam.update(g, optim_stepsize * cur_lrmult)
losses.append(newlosses)
logger.log(fmt_row(13, np.mean(losses, axis=0)))
logger.log("Evaluating losses...")
losses = []
for batch in d.iterate_once(optim_batchsize):
newlosses = compute_losses(batch["ob"], batch["ac"],
batch["atarg"], batch["vtarg"],
cur_lrmult)
losses.append(newlosses)
meanlosses, _, _ = mpi_moments(losses, axis=0)
logger.log(fmt_row(13, meanlosses))
for (lossval, name) in zipsame(meanlosses, loss_names):
logger.record_tabular("loss_" + name, lossval)
logger.record_tabular("ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews = map(flatten_lists, zip(*listoflrpairs))
if reward_drop_bound is None:
lenbuffer.extend(lens)
rewbuffer.extend(rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
logger.record_tabular("Iter", iters_so_far)
if positive_rew_enforce:
if adjust_ratio is not None:
logger.record_tabular("RewardAdjustRatio", adjust_ratio)
if MPI.COMM_WORLD.Get_rank() == 0:
logger.dump_tabular()
if max_threshold is not None:
print('Current max return: ', np.max(rewbuffer))
if np.max(rewbuffer) > max_threshold:
max_thres_satisfied = True
else:
max_thres_satisfied = False
return_threshold_satisfied = True
if return_threshold is not None:
if not (np.mean(rewbuffer) > return_threshold
and iters_so_far > min_iters):
return_threshold_satisfied = False
rollout_length_thershold_satisfied = True
if rollout_length_thershold is not None:
rewlocal = (seg["avg_vels"], seg["rew"]) # local values
listofrews = MPI.COMM_WORLD.allgather(rewlocal) # list of tuples
avg_vels, rews = map(flatten_lists, zip(*listofrews))
if not (np.mean(lenbuffer) > rollout_length_thershold
and np.mean(avg_vels) > 0.5 * env.env.env.final_tv):
rollout_length_thershold_satisfied = False
if rollout_length_thershold is not None or return_threshold is not None:
if rollout_length_thershold_satisfied and return_threshold_satisfied:
break
return pi, np.mean(rewbuffer)
def learn(policy,
env,
seed,
ob_space,
ac_space,
nsteps=5,
total_timesteps=int(80e6),
vf_coef=0.5,
ent_coef=0.01,
max_grad_norm=0.5,
lr=7e-4,
lrschedule='linear',
epsilon=1e-5,
alpha=0.99,
gamma=0.99,
log_interval=100,
save_dir=None):
set_global_seeds(seed)
nenvs = env.num_envs
#ob_space = env.observation_space
#ac_space = env.action_space
model = Model(policy=policy,
ob_space=ob_space,
ac_space=ac_space,
nenvs=nenvs,
nsteps=nsteps,
ent_coef=ent_coef,
vf_coef=vf_coef,
max_grad_norm=max_grad_norm,
lr=lr,
alpha=alpha,
epsilon=epsilon,
total_timesteps=total_timesteps,
lrschedule=lrschedule)
runner = Runner(env, model, ob_space=ob_space, nsteps=nsteps, gamma=gamma)
nbatch = nenvs * nsteps
tstart = time.time()
episode_stats = EpisodeStats(nsteps, nenvs)
for update in range(1, total_timesteps // nbatch + 1):
obs, states, rewards, masks, actions, values, raw_rewards = runner.run(
)
episode_stats.feed(raw_rewards, masks)
policy_loss, value_loss, policy_entropy = model.train(
obs, states, rewards, masks, actions, values)
nseconds = time.time() - tstart
fps = int((update * nbatch) / nseconds)
if update % log_interval == 0 or update == 1:
ev = explained_variance(values, rewards)
logger.record_tabular("nupdates", update)
logger.record_tabular("total_timesteps", update * nbatch)
logger.record_tabular("fps", fps)
logger.record_tabular("policy_entropy", float(policy_entropy))
logger.record_tabular("value_loss", float(value_loss))
logger.record_tabular("explained_variance", float(ev))
logger.record_tabular("episode_reward",
episode_stats.mean_reward())
logger.record_tabular("episode_length",
episode_stats.mean_length())
logger.dump_tabular()
model.save(save_dir)
env.close()
return model
def learn(*,
network,
env,
total_timesteps,
eval_env=None,
seed=None,
nsteps=2048,
ent_coef=0.0,
lr=3e-4,
vf_coef=0.5,
max_grad_norm=0.5,
gamma=0.99,
lam=0.95,
log_interval=10,
nminibatches=4,
noptepochs=4,
cliprange=0.2,
save_interval=0,
load_path=None,
model_fn=None,
log_path=None,
**network_kwargs):
'''
Learn policy using PPO algorithm (https://arxiv.org/abs/1707.06347)
Parameters:
----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See common/models.py/lstm for more details on using recurrent nets in policies
env: baselines.common.vec_env.VecEnv environment. Needs to be vectorized for parallel environment simulation.
The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class.
nsteps: int number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel)
total_timesteps: int number of timesteps (i.e. number of actions taken in the environment)
ent_coef: float policy entropy coefficient in the optimization objective
lr: float or function learning rate, constant or a schedule function [0,1] -> R+ where 1 is beginning of the
training and 0 is the end of the training.
vf_coef: float value function loss coefficient in the optimization objective
max_grad_norm: float or None gradient norm clipping coefficient
gamma: float discounting factor
lam: float advantage estimation discounting factor (lambda in the paper)
log_interval: int number of timesteps between logging events
nminibatches: int number of training minibatches per update. For recurrent policies,
should be smaller or equal than number of environments run in parallel.
noptepochs: int number of training epochs per update
cliprange: float or function clipping range, constant or schedule function [0,1] -> R+ where 1 is beginning of the training
and 0 is the end of the training
save_interval: int number of timesteps between saving events
load_path: str path to load the model from
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
'''
set_global_seeds(seed)
if isinstance(lr, float): lr = constfn(lr)
else: assert callable(lr)
if isinstance(cliprange, float): cliprange = constfn(cliprange)
else: assert callable(cliprange)
total_timesteps = int(total_timesteps)
# Get the nb of env
nenvs = env.num_envs
# Get state_space and action_space
ob_space = env.observation_space
ac_space = env.action_space
if isinstance(network, str):
network_type = network
policy_network_fn = get_network_builder(network_type)(**network_kwargs)
network = policy_network_fn(ob_space.shape)
else:
network = network(ob_space.shape)
# Calculate the batch_size
nbatch = nenvs * nsteps
nbatch_train = nbatch // nminibatches
is_mpi_root = (MPI is None or MPI.COMM_WORLD.Get_rank() == 0)
# Instantiate the model object (that creates act_model and train_model)
if model_fn is None:
from baselines.ppo2.model import Model
model_fn = Model
model = model_fn(ac_space=ac_space,
policy_network=network,
ent_coef=ent_coef,
vf_coef=vf_coef,
max_grad_norm=max_grad_norm)
logdir = log_path + '/evaluator'
modeldir = log_path + '/models'
if not os.path.exists(logdir):
os.makedirs(logdir)
if not os.path.exists(modeldir):
os.makedirs(modeldir)
evaluator = Evaluator(env=eval_env, model=model, logdir=logdir)
max_inner_iter = 500000 if env.spec.id == 'InvertedDoublePendulum-v2' else 3000000
epoch = noptepochs
inner_iter_per_iter = epoch * nminibatches
max_iter = int(max_inner_iter / inner_iter_per_iter)
eval_num = 150
eval_interval = save_interval = int(
int(max_inner_iter / eval_num) / inner_iter_per_iter)
if load_path is not None:
load_path = osp.expanduser(load_path)
ckpt = tf.train.Checkpoint(model=model)
manager = tf.train.CheckpointManager(ckpt, load_path, max_to_keep=None)
ckpt.restore(manager.latest_checkpoint)
# Instantiate the runner object
runner = Runner(env=env, model=model, nsteps=nsteps, gamma=gamma, lam=lam)
epinfobuf = deque(maxlen=100)
# Start total timer
tfirststart = time.perf_counter()
nupdates = total_timesteps // nbatch
for update in range(1, max_iter + 1):
assert nbatch % nminibatches == 0
# Start timer
tstart = time.perf_counter()
frac = 1.0 - (update - 1.0) / nupdates
# Calculate the learning rate
lrnow = lr(frac)
cliprangenow = cliprange(frac)
if update % log_interval == 0 and is_mpi_root:
logger.info('Stepping environment...')
if (update - 1) % eval_interval == 0:
evaluator.run_evaluation(update - 1)
if (update - 1) % save_interval == 0:
ckpt = tf.train.Checkpoint(model=model)
ckpt.save(modeldir + '/ckpt_ite' + str((update - 1)))
# Get minibatch
obs, returns, masks, actions, values, neglogpacs, states, epinfos = runner.run(
) #pylint: disable=E0632
epinfobuf.extend(epinfos)
# Here what we're going to do is for each minibatch calculate the loss and append it.
mblossvals = []
if states is None: # nonrecurrent version
# Index of each element of batch_size
# Create the indices array
inds = np.arange(nbatch)
for _ in range(noptepochs):
# Randomize the indexes
np.random.shuffle(inds)
# 0 to batch_size with batch_train_size step
for start in range(0, nbatch, nbatch_train):
end = start + nbatch_train
mbinds = inds[start:end]
slices = (tf.constant(arr[mbinds])
for arr in (obs, returns, masks, actions, values,
neglogpacs))
mblossvals.append(model.train(lrnow, cliprangenow,
*slices))
else: # recurrent version
raise ValueError('Not Support Yet')
# Feedforward --> get losses --> update
lossvals = np.mean(mblossvals, axis=0)
# End timer
tnow = time.perf_counter()
# Calculate the fps (frame per second)
fps = int(nbatch / (tnow - tstart))
if update % log_interval == 0 or update == 1:
# Calculates if value function is a good predicator of the returns (ev > 1)
# or if it's just worse than predicting nothing (ev =< 0)
ev = explained_variance(values, returns)
logger.logkv("misc/serial_timesteps", update * nsteps)
logger.logkv("misc/nupdates", update)
logger.logkv("misc/total_timesteps", update * nbatch)
logger.logkv("fps", fps)
logger.logkv("misc/explained_variance", float(ev))
logger.logkv('eprewmean',
safemean([epinfo['r'] for epinfo in epinfobuf]))
logger.logkv('eplenmean',
safemean([epinfo['l'] for epinfo in epinfobuf]))
logger.logkv('misc/time_elapsed', tnow - tfirststart)
for (lossval, lossname) in zip(lossvals, model.loss_names):
logger.logkv('loss/' + lossname, lossval)
logger.dumpkvs()
return model
def learn(policy,
env,
seed,
total_timesteps=int(40e6),
gamma=0.99,
log_interval=1,
nprocs=24,
nscripts=12,
nsteps=20,
nstack=4,
ent_coef=0.01,
vf_coef=0.5,
vf_fisher_coef=1.0,
lr=0.25,
max_grad_norm=0.01,
kfac_clip=0.001,
save_interval=None,
lrschedule='linear',
callback=None):
tf.reset_default_graph()
set_global_seeds(seed)
nenvs = nprocs
ob_space = (32, 32, 3) # env.observation_space
ac_space = (32, 32)
make_model = lambda: Model(policy,
ob_space,
ac_space,
nenvs,
total_timesteps,
nprocs=nprocs,
nscripts=nscripts,
nsteps=nsteps,
nstack=nstack,
ent_coef=ent_coef,
vf_coef=vf_coef,
vf_fisher_coef=vf_fisher_coef,
lr=lr,
max_grad_norm=max_grad_norm,
kfac_clip=kfac_clip,
lrschedule=lrschedule)
if save_interval and logger.get_dir():
import cloudpickle
with open(osp.join(logger.get_dir(), 'make_model.pkl'), 'wb') as fh:
fh.write(cloudpickle.dumps(make_model))
model = make_model()
print("make_model complete!")
runner = Runner(env,
model,
nsteps=nsteps,
nscripts=nscripts,
nstack=nstack,
gamma=gamma,
callback=callback)
nbatch = nenvs * nsteps
tstart = time.time()
# enqueue_threads = model.q_runner.create_threads(model.sess, coord=tf.train.Coordinator(), start=True)
for update in range(1, total_timesteps // nbatch + 1):
obs, states, td_targets, masks, actions, xy0, xy1, values = runner.run(
)
policy_loss, value_loss, policy_entropy, \
policy_loss_xy0, policy_entropy_xy0, \
policy_loss_xy1, policy_entropy_xy1, \
= model.train(obs, states, td_targets,
masks, actions,
xy0, xy1, values)
model.old_obs = obs
nseconds = time.time() - tstart
fps = int((update * nbatch) / nseconds)
if update % log_interval == 0 or update == 1:
ev = explained_variance(values, td_targets)
# nsml.report(
# nupdates=update,
# total_timesteps=update * nbatch,
# fps=fps,
# policy_entropy=float(policy_entropy),
# policy_loss=float(policy_loss),
# policy_loss_xy0=float(policy_loss_xy0),
# policy_entropy_xy0=float(policy_entropy_xy0),
# policy_loss_xy1=float(policy_loss_xy1),
# policy_entropy_xy1=float(policy_entropy_xy1),
# value_loss=float(value_loss),
# explained_variance=float(ev),
# batch_size=nbatch,
# step=update,
# scope=locals()
# )
# logger.record_tabular("nupdates", update)
# logger.record_tabular("total_timesteps", update * nbatch)
# logger.record_tabular("fps", fps)
# logger.record_tabular("policy_entropy", float(policy_entropy))
# logger.record_tabular("policy_loss", float(policy_loss))
# logger.record_tabular("policy_loss_xy0", float(policy_loss_xy0))
# logger.record_tabular("policy_entropy_xy0",
# float(policy_entropy_xy0))
# logger.record_tabular("policy_loss_xy1", float(policy_loss_xy1))
# logger.record_tabular("policy_entropy_xy1",
# float(policy_entropy_xy1))
# # logger.record_tabular("policy_loss_y0", float(policy_loss_y0))
# # logger.record_tabular("policy_entropy_y0", float(policy_entropy_y0))
# logger.record_tabular("value_loss", float(value_loss))
# logger.record_tabular("explained_variance", float(ev))
# logger.dump_tabular()
if save_interval and (update % save_interval == 0
or update == 1) and logger.get_dir():
savepath = osp.join(logger.get_dir(), 'checkpoint%.5i' % update)
print('Saving to', savepath)
model.save(savepath)
env.close()
def learn(
env,
policy_fn,
*,
timesteps_per_batch, # what to train on
max_kl,
cg_iters,
gamma,
lam, # advantage estimation
entcoeff=0.0,
cg_damping=1e-2,
vf_stepsize=3e-4,
vf_iters=3,
max_timesteps=0,
max_episodes=0,
max_iters=0, # time constraint
callback=None,
tester=None):
nworkers = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
np.set_printoptions(precision=3)
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_fn("pi", ob_space, ac_space)
oldpi = policy_fn("oldpi", ob_space, ac_space)
atarg = tf.placeholder(
dtype=tf.float32,
shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
ob = U.get_placeholder_cached(name="ob")
ac = pi.pdtype.sample_placeholder([None])
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent) # ent: entropy
# penalty coeffcient ??
entbonus = entcoeff * meanent
# value function error.
vferr = tf.reduce_mean(tf.square(pi.vpred - ret))
# logp: return log value of the policy in action: ac.
# this is to do important sampling:
ratio = tf.exp(pi.pd.logp(ac) -
oldpi.pd.logp(ac)) # advantage * pnew / pold
# surrgain: loss for specific action:
surrgain = tf.reduce_mean(ratio * atarg)
optimgain = surrgain + entbonus
losses = [optimgain, meankl, entbonus, surrgain, meanent]
loss_names = ["optimgain", "meankl", "entloss", "surrgain", "entropy"]
dist = meankl
all_var_list = pi.get_trainable_variables()
# pol: policy
# vf: value function
var_list = [
v for v in all_var_list if v.name.split("/")[1].startswith("pol")
]
vf_var_list = [
v for v in all_var_list if v.name.split("/")[1].startswith("vf")
]
vfadam = MpiAdam(vf_var_list)
get_flat = U.GetFlat(var_list)
set_from_flat = U.SetFromFlat(var_list)
klgrads = tf.gradients(dist, var_list)
flat_tangent = tf.placeholder(dtype=tf.float32,
shape=[None],
name="flat_tan")
shapes = [var.get_shape().as_list() for var in var_list]
start = 0
tangents = []
# reshape flat_tangent into shape of var_list
for shape in shapes:
# product all of element in x
sz = U.intprod(shape)
tangents.append(tf.reshape(flat_tangent[start:start + sz], shape))
start += sz
# fvp : create op to calculate fisher_vector_product
gvp = tf.add_n([
tf.reduce_sum(g * tangent)
for (g, tangent) in zipsame(klgrads, tangents)
]) #pylint: disable=E1111
fvp = U.flatgrad(gvp, var_list)
assign_old_eq_new = U.function(
[], [],
updates=[
tf.assign(oldv, newv)
for (oldv,
newv) in zipsame(oldpi.get_variables(), pi.get_variables())
])
# we should input the same size of observation action and advantage correct in correspondence with the <observation, action>.
compute_losses = U.function([ob, ac, atarg], losses)
compute_lossandgrad = U.function([ob, ac, atarg], losses +
[U.flatgrad(optimgain, var_list)])
compute_fvp = U.function([flat_tangent, ob, ac, atarg], fvp)
compute_vflossandgrad = U.function([ob, ret],
U.flatgrad(vferr, vf_var_list))
@contextmanager
def timed(msg):
if rank == 0:
print(colorize(msg, color='magenta'))
tstart = time.time()
yield
print(
colorize("done in %.3f seconds" % (time.time() - tstart),
color='magenta'))
else:
yield
def allmean(x):
assert isinstance(x, np.ndarray)
out = np.empty_like(x)
MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
out /= nworkers
return out
U.initialize()
th_init = get_flat()
MPI.COMM_WORLD.Bcast(th_init, root=0)
set_from_flat(th_init)
vfadam.sync()
print("Init param sum", th_init.sum(), flush=True)
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi,
env,
timesteps_per_batch,
stochastic=True,
time_step_holder=tester.time_step_holder)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=40) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=40) # rolling buffer for episode rewards
assert sum([max_iters > 0, max_timesteps > 0, max_episodes > 0]) == 1
while True:
if callback: callback(locals(), globals())
if max_timesteps and timesteps_so_far >= max_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
logger.log("********** Iteration %i ************" % iters_so_far)
with timed("sampling"):
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, gamma, lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg[
"tdlamret"]
vpredbefore = seg["vpred"] # predicted value function before udpate
# advantage target function.
atarg = (atarg - atarg.mean()
) / atarg.std() # standardized advantage function estimate
if hasattr(pi, "ret_rms"):
pi.ret_rms.update(tdlamret)
if hasattr(pi, "ob_rms"):
pi.ob_rms.update(ob) # update running mean/std for policy
args = seg["ob"], seg["ac"], atarg
fvpargs = [arr[::5] for arr in args]
def fisher_vector_product(p):
return allmean(compute_fvp(p, *fvpargs)) + cg_damping * p
assign_old_eq_new() # set old parameter values to new parameter values
with timed("computegrad"):
*lossbefore, g = compute_lossandgrad(*args)
lossbefore = allmean(np.array(lossbefore))
g = allmean(g)
if np.allclose(g, 0):
logger.log("Got zero gradient. not updating")
else:
with timed("cg"):
# calculate fisher vector product use iterate method.
stepdir = cg(fisher_vector_product,
g,
cg_iters=cg_iters,
verbose=rank == 0)
assert np.isfinite(stepdir).all()
shs = .5 * stepdir.dot(fisher_vector_product(stepdir))
lm = np.sqrt(shs / max_kl)
# logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g))
fullstep = stepdir / lm
expectedimprove = g.dot(fullstep)
surrbefore = lossbefore[0]
stepsize = 1.0
# get flat policy variable list
thbefore = get_flat()
# try to update with shrinking step size.
for _ in range(10):
thnew = thbefore + fullstep * stepsize
# update policy variable list.
set_from_flat(thnew)
# surr: objective loss
# kl: mean kl value
meanlosses = surr, kl, *_ = allmean(
np.array(compute_losses(*args)))
improve = surr - surrbefore
logger.log("Expected: %.3f Actual: %.3f" %
(expectedimprove, improve))
if not np.isfinite(meanlosses).all():
logger.log("Got non-finite value of losses -- bad!")
# note: constraint will be satisfied use this function.
elif kl > max_kl * 1.5:
logger.log("violated KL constraint. shrinking step.")
elif improve < 0:
logger.log("surrogate didn't improve. shrinking step.")
else:
logger.log("Stepsize OK!")
break
stepsize *= .5
else:
logger.log("couldn't compute a good step")
set_from_flat(thbefore)
if nworkers > 1 and iters_so_far % 20 == 0:
paramsums = MPI.COMM_WORLD.allgather(
(thnew.sum(), vfadam.getflat().sum())) # list of tuples
assert all(
np.allclose(ps, paramsums[0]) for ps in paramsums[1:])
for (lossname, lossval) in zip(loss_names, meanlosses):
logger.record_tabular(lossname, lossval)
with timed("vf"):
# update value function estimator.
for _ in range(vf_iters):
for (mbob, mbret) in dataset.iterbatches(
(seg["ob"], seg["tdlamret"]),
include_final_partial_batch=False,
batch_size=64):
g = allmean(compute_vflossandgrad(mbob, mbret))
vfadam.update(g, vf_stepsize)
logger.record_tabular("ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
if iters_so_far % 10 == 0:
tester.check_and_test(pi, always=True)
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
if rank == 0:
logger.dump_tabular()
def learn(*,
network,
env,
total_timesteps,
expert,
eval_env=None,
seed=None,
nsteps=2048,
ent_coef=0.0,
lr=3e-4,
vf_coef=0.5,
max_grad_norm=0.5,
gamma=0.99,
lam=0.95,
log_interval=10,
nminibatches=4,
noptepochs=4,
cliprange=0.2,
save_interval=0,
load_path=None,
model_fn=None,
**network_kwargs):
'''
Learn policy using PPO algorithm (https://arxiv.org/abs/1707.06347)
Expert is an input to the network but not explictly used in step function.
Parameters:
----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See common/models.py/lstm for more details on using recurrent nets in policies
env: baselines.common.vec_env.VecEnv environment. Needs to be vectorized for parallel environment simulation.
The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class.
nsteps: int number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel)
total_timesteps: int number of timesteps (i.e. number of actions taken in the environment)
ent_coef: float policy entropy coefficient in the optimization objective
lr: float or function learning rate, constant or a schedule function [0,1] -> R+ where 1 is beginning of the
training and 0 is the end of the training.
vf_coef: float value function loss coefficient in the optimization objective
max_grad_norm: float or None gradient norm clipping coefficient
gamma: float discounting factor
lam: float advantage estimation discounting factor (lambda in the paper)
log_interval: int number of timesteps between logging events
nminibatches: int number of training minibatches per update. For recurrent policies,
should be smaller or equal than number of environments run in parallel.
noptepochs: int number of training epochs per update
cliprange: float or function clipping range, constant or schedule function [0,1] -> R+ where 1 is beginning of the training
and 0 is the end of the training
save_interval: int number of timesteps between saving events
load_path: str path to load the model from
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
'''
set_global_seeds(seed)
if isinstance(lr, float): lr = constfn(lr)
else: assert callable(lr)
if isinstance(cliprange, float): cliprange = constfn(cliprange)
else: assert callable(cliprange)
total_timesteps = int(total_timesteps)
policy = build_policy(env, network, **network_kwargs)
# Get the nb of env
nenvs = env.num_envs
# Get state_space and action_space
ob_space = env.observation_space
ac_space = env.action_space
ob_space = Box(
np.concatenate([env.observation_space.low, env.action_space.low]),
np.concatenate([env.observation_space.high, env.action_space.high]),
dtype=np.float32)
# Calculate the batch_size
nbatch = nenvs * nsteps
nbatch_train = nbatch // nminibatches
# Instantiate the model object (that creates act_model and train_model)
if model_fn is None:
from brl_baselines.bppo2_expert.model import Model
model_fn = Model
model = model_fn(policy=policy,
ob_space=ob_space,
ac_space=ac_space,
nbatch_act=nenvs,
nbatch_train=nbatch_train,
nsteps=nsteps,
ent_coef=ent_coef,
vf_coef=vf_coef,
max_grad_norm=max_grad_norm)
if load_path is not None:
model.load(load_path)
# Instantiate the runner object
runner = Runner(env=env,
model=model,
nsteps=nsteps,
gamma=gamma,
lam=lam,
expert=expert)
if eval_env is not None:
eval_runner = Runner(env=eval_env,
model=model,
nsteps=nsteps,
gamma=gamma,
lam=lam,
expert=expert)
epinfobuf = deque(maxlen=100)
if eval_env is not None:
eval_epinfobuf = deque(maxlen=100)
# Start total timer
tfirststart = time.time()
nupdates = total_timesteps // nbatch
for update in range(1, nupdates + 1):
print("update", update)
assert nbatch % nminibatches == 0
# Start timer
tstart = time.time()
frac = 1.0 - (update - 1.0) / nupdates
# Calculate the learning rate
lrnow = lr(frac)
# Calculate the cliprange
cliprangenow = cliprange(frac)
# Get minibatch
obs, returns, masks, actions, values, neglogpacs, states, epinfos = runner.run(
) #pylint: disable=E0632
if eval_env is not None:
eval_obs, eval_returns, eval_masks, eval_actions, eval_values, eval_neglogpacs, eval_states, eval_epinfos = eval_runner.run(
) #pylint: disable=E0632
epinfobuf.extend(epinfos)
if eval_env is not None:
eval_epinfobuf.extend(eval_epinfos)
# Here what we're going to do is for each minibatch calculate the loss and append it.
mblossvals = []
if states is None: # nonrecurrent version
# Index of each element of batch_size
# Create the indices array
inds = np.arange(nbatch)
for _ in range(noptepochs):
# Randomize the indexes
np.random.shuffle(inds)
# 0 to batch_size with batch_train_size step
for start in range(0, nbatch, nbatch_train):
end = start + nbatch_train
mbinds = inds[start:end]
slices = (arr[mbinds]
for arr in (obs, returns, masks, actions, values,
neglogpacs))
mblossvals.append(model.train(lrnow, cliprangenow,
*slices))
else: # recurrent version
assert nenvs % nminibatches == 0
envsperbatch = nenvs // nminibatches
envinds = np.arange(nenvs)
flatinds = np.arange(nenvs * nsteps).reshape(nenvs, nsteps)
envsperbatch = nbatch_train // nsteps
for _ in range(noptepochs):
np.random.shuffle(envinds)
for start in range(0, nenvs, envsperbatch):
end = start + envsperbatch
mbenvinds = envinds[start:end]
mbflatinds = flatinds[mbenvinds].ravel()
slices = (arr[mbflatinds]
for arr in (obs, returns, masks, actions, values,
neglogpacs))
mbstates = states[mbenvinds]
mblossvals.append(
model.train(lrnow, cliprangenow, *slices, mbstates))
# Feedforward --> get losses --> update
lossvals = np.mean(mblossvals, axis=0)
# End timer
tnow = time.time()
# Calculate the fps (frame per second)
fps = int(nbatch / (tnow - tstart))
if update % log_interval == 0 or update == 1:
# Calculates if value function is a good predicator of the returns (ev > 1)
# or if it's just worse than predicting nothing (ev =< 0)
ev = explained_variance(values, returns)
logger.logkv("serial_timesteps", update * nsteps)
logger.logkv("nupdates", update)
logger.logkv("total_timesteps", update * nbatch)
logger.logkv("fps", fps)
logger.logkv("explained_variance", float(ev))
logger.logkv('eprewmean',
safemean([epinfo['r'] for epinfo in epinfobuf]))
logger.logkv('eplenmean',
safemean([epinfo['l'] for epinfo in epinfobuf]))
if eval_env is not None:
logger.logkv(
'eval_eprewmean',
safemean([epinfo['r'] for epinfo in eval_epinfobuf]))
logger.logkv(
'eval_eplenmean',
safemean([epinfo['l'] for epinfo in eval_epinfobuf]))
logger.logkv('time_elapsed', tnow - tfirststart)
for (lossval, lossname) in zip(lossvals, model.loss_names):
logger.logkv(lossname, lossval)
if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
logger.dumpkvs()
if save_interval and (update % save_interval == 0
or update == 1) and logger.get_dir() and (
MPI is None
or MPI.COMM_WORLD.Get_rank() == 0):
checkdir = osp.join(logger.get_dir(), 'checkpoints')
os.makedirs(checkdir, exist_ok=True)
savepath = osp.join(checkdir, '%.5i' % update)
print('Saving to', savepath)
model.save(savepath)
print('eprewmean', safemean([epinfo['r'] for epinfo in epinfobuf]))
print('eplenmean', safemean([epinfo['l'] for epinfo in epinfobuf]))
return model
def learn(*,
network,
env,
total_timesteps,
dtarg=0.01,
adaptive_kl=0,
clipcut=None,
trunc_rho=1.0,
useadv=0,
vtrace=0,
rgae=0,
eval_env=None,
seed=None,
ERlen=1,
nsteps=2048,
ent_coef=0.0,
lr=3e-4,
vf_coef=0.5,
max_grad_norm=None,
gamma=0.99,
lam=0.95,
log_interval=10,
nminibatches=4,
noptepochs=4,
cliprange=0.2,
save_interval=0,
load_path=None,
**network_kwargs):
'''
Learn policy using PPO algorithm (https://arxiv.org/abs/1707.06347)
Parameters:
----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See common/models.py/lstm for more details on using recurrent nets in policies
env: baselines.common.vec_env.VecEnv environment. Needs to be vectorized for parallel environment simulation.
The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class.
nsteps: int number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel)
total_timesteps: int number of timesteps (i.e. number of actions taken in the environment)
ent_coef: float policy entropy coefficient in the optimization objective
lr: float or function learning rate, constant or a schedule function [0,1] -> R+ where 1 is beginning of the
training and 0 is the end of the training.
vf_coef: float value function loss coefficient in the optimization objective
max_grad_norm: float or None gradient norm clipping coefficient
gamma: float discounting factor
lam: float advantage estimation discounting factor (lambda in the paper)
log_interval: int number of timesteps between logging events
nminibatches: int number of training minibatches per update. For recurrent policies,
should be smaller or equal than number of environments run in parallel.
noptepochs: int number of training epochs per update
cliprange: float or function clipping range, constant or schedule function [0,1] -> R+ where 1 is beginning of the training
and 0 is the end of the training
save_interval: int number of timesteps between saving events
load_path: str path to load the model from
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
'''
set_global_seeds(seed)
if isinstance(lr, float): lr = constfn(lr)
else: assert callable(lr)
if isinstance(cliprange, float): cliprange = constfn(cliprange)
else: assert callable(cliprange)
total_timesteps = int(total_timesteps)
policy = build_policy(env, network, **network_kwargs)
# Get the nb of env
nenvs = 1
# Get state_space and action_space
ob_space = env.observation_space
ac_space = env.action_space
acdim = ac_space.shape[0]
# Calculate the batch_size
nbatch = nenvs * nsteps
nbatch_train = nbatch // nminibatches
# Instantiate the model object (that creates act_model and train_model)
make_model = lambda: Model(policy=policy,
ob_space=ob_space,
ac_space=ac_space,
nbatch_act=nenvs,
nbatch_train=nbatch_train,
nsteps=nsteps,
ent_coef=ent_coef,
vf_coef=vf_coef,
max_grad_norm=max_grad_norm,
adaptive_kl=adaptive_kl)
model = make_model()
if load_path is not None:
model.load(load_path)
# Instantiate the runner object
runner = Runner(env=env, model=model, nsteps=nsteps, gamma=gamma, lam=lam)
if eval_env is not None:
eval_runner = EvalRunner(env=eval_env,
model=model,
nsteps=10 * nsteps,
gamma=gamma,
lam=lam)
eval_runner.obfilt = runner.obfilt
eval_runner.rewfilt = runner.rewfilt
epinfobuf = deque(maxlen=10)
if eval_env is not None:
eval_epinfobuf = deque(maxlen=10)
# Start total timer
tfirststart = time.time()
nupdates = total_timesteps // nbatch
def add_vtarg_and_adv(seg, gamma, value, lam):
"""
Compute target value using TD(lambda) estimator, and advantage with GAE(lambda)
"""
done = np.append(
seg["done"], 0
) # last element is only used for last vtarg, but we already zeroed it if last new = 1
T = len(seg["rew"])
gaelam = np.empty(T, 'float32')
rew = runner.rewfilt(seg["rew"])
lastgaelam = 0
for t in reversed(range(T)):
nonterminal = 1 - done[t + 1]
delta = rew[t] + gamma * value[t + 1] * nonterminal - value[t]
gaelam[
t] = lastgaelam = delta + gamma * lam * nonterminal * lastgaelam
ret = gaelam + value[:-1]
return gaelam, ret
def add_vtarg_and_adv_vtrace(seg,
gamma,
value,
rho,
trunc_rho,
acdim=None):
"""
Compute target value using TD(lambda) estimator, and advantage with GAE(lambda)
"""
done = np.append(
seg["done"], 0
) # last element is only used for last vtarg, but we already zeroed it if last new = 1
rho_ = np.append(rho, 1.0)
if acdim is not None:
rho_ = np.exp(np.log(rho_) / acdim)
r = np.minimum(trunc_rho, rho_)
c = lam * np.minimum(1.0, rho_)
T = len(seg["rew"])
gaelam = np.empty(T, 'float32')
gaelam2 = np.empty(T, 'float32')
rew = runner.rewfilt(seg["rew"])
lastgaelam = 0
for t in reversed(range(T)):
nonterminal = 1 - done[t + 1]
delta = (rew[t] + gamma * value[t + 1] * nonterminal - value[t])
gaelam[t] = delta + gamma * lam * nonterminal * lastgaelam
lastgaelam = r[t] * gaelam[t]
ret = r[:-1] * gaelam + value[:-1]
adv = rew + gamma * (1.0 - done[1:]) * np.hstack([ret[1:], value[T]
]) - value[:-1]
return adv, ret, gaelam
def add_vtarg_and_adv_vtrace4(seg,
gamma,
value,
rho,
trunc_rho,
acdim=None):
"""
Compute target value using TD(lambda) estimator, and advantage with GAE(lambda)
"""
done = np.append(
seg["done"], 0
) # last element is only used for last vtarg, but we already zeroed it if last new = 1
rho_ = np.append(rho, 1.0)
if acdim is not None:
rho_ = np.exp(np.log(rho_) / acdim)
T = len(seg["rew"])
gaelam = np.zeros(T, 'float32')
rew = runner.rewfilt(seg["rew"])
delta = (rew + gamma * value[1:] * (1.0 - done[1:]) - value[:-1])
gamlam = np.zeros(T, 'float32')
for i in range(T):
gamlam[i] = (gamma * lam)**i
idx = T
c = np.ones(T)
for t in reversed(range(T)):
# print(delta2)
for j in range(t, T):
if done[j + 1]:
idx = j + 1
break
gaelam[t] = np.sum(gamlam[:idx - t] *
(np.minimum(1.0, c) * delta)[t:idx])
c[t:] = rho_[t] * c[t:]
ret = np.minimum(trunc_rho, rho_[:-1]) * gaelam + value[:-1]
adv = rew + gamma * (1.0 - done[1:]) * np.hstack([ret[1:], value[T]
]) - value[:-1]
return adv, ret, gaelam
seg = None
cliprangenow = cliprange(1.0)
klconst = 1.0
batch_IS = []
not_use = []
for update in range(1, nupdates + 1):
assert nbatch % nminibatches == 0
# Start timer
tstart = time.time()
frac = 1.0 - (update - 1.0) / nupdates
# Calculate the learning rate
lrnow = np.maximum(1e-4, lr(frac))
# Calculate the cliprange
# Get minibatch
if seg is None:
prev_seg = seg
seg = {}
else:
prev_seg = {}
for i in seg:
prev_seg[i] = np.copy(seg[i])
seg["ob"], seg["rew"], seg["done"], seg["ac"], seg["neglogp"], seg[
"mean"], seg[
"logstd"], final_obs, final_done, epinfos = runner.run() #pylint: disable=E0632
# print(np.shape(seg["ob"]))
if prev_seg is not None:
for key in seg:
if len(np.shape(seg[key])) == 1:
seg[key] = np.hstack([prev_seg[key], seg[key]])
else:
seg[key] = np.vstack([prev_seg[key], seg[key]])
if np.shape(seg[key])[0] > ERlen * nsteps:
seg[key] = seg[key][-ERlen * nsteps:]
ob_stack = np.vstack([seg["ob"], final_obs])
values = model.values(runner.obfilt(ob_stack))
values[:-1] = (1.0 - final_done) * values[:-1]
mean_now, logstd_now = model.meanlogstds(runner.obfilt(seg["ob"]))
# print(np.shape(seg["ac"])[1])
neglogpnow = 0.5 * np.sum(np.square((seg["ac"] - mean_now) / np.exp(logstd_now)), axis=-1) \
+ 0.5 * np.log(2.0 * np.pi) * np.shape(seg["ac"])[1] \
+ np.sum(logstd_now, axis=-1)
rho = np.exp(-neglogpnow + seg["neglogp"])
# print(len(mean_now))
# print(cliprangenow)
# print(rho)
if vtrace == 1:
adv, ret, gae = add_vtarg_and_adv_vtrace(seg, gamma, values, rho,
trunc_rho)
if useadv:
gae = adv
elif vtrace == 4:
adv, ret, gae = add_vtarg_and_adv_vtrace4(seg, gamma, values, rho,
trunc_rho)
if useadv:
gae = adv
else:
gae, ret = add_vtarg_and_adv(seg, gamma, values, lam)
r = np.minimum(1.0, rho)
r_gae = gae * r
# print("======")
# print(gae)
# print(r_gae)
# print(gae.mean())
# print(r_gae.mean())
# print(gae.std())
# print(r_gae.std())
# print(r.mean())
# print("======")
if eval_env is not None:
eval_obs, eval_returns, eval_masks, eval_actions, _, _, eval_epinfos = eval_runner.run(
) #pylint: disable=E0632
prior_row = np.zeros(len(seg["ob"]))
temp_prior = []
for i in range(int(len(prior_row) / nsteps)):
temp_row = np.mean(
np.abs(rho[i * nsteps:(i + 1) * nsteps] - 1.0) + 1.0)
# local_rho[i + (ERlen-int(len(prior_row)/nsteps))].append(temp_row)
print(temp_row)
temp_prior.append(temp_row)
if temp_row > 1 + 0.2:
prior_row[i * nsteps:(i + 1) * nsteps] = 0
else:
prior_row[i * nsteps:(i + 1) * nsteps] = 1
prior_row[i * nsteps:(i + 1) * nsteps] = 1
# print(prior_row)
# for i in range(len(prior_row)):
# if (np.abs(rho[i] - 1.0) + 1.0)>1.05:
# prior_row[i]=0
# else:
# prior_row[i]=1
# for i in range(len(prior_row)):
# if rho[i]>1.1 :
# prior_row[i]=0
# else:
# prior_row[i]=1
prob = prior_row / np.sum(prior_row)
# print(np.sum(prior_row))
epinfobuf.extend(epinfos)
if eval_env is not None:
eval_epinfobuf.extend(eval_epinfos)
# Here what we're going to do is for each minibatch calculate the loss and append it.
mblossvals = []
# Index of each element of batch_size
# Create the indices array
inds1 = np.arange(len(seg["ob"]) - nsteps)
inds2 = np.arange(nsteps) + len(seg["ob"]) - nsteps
# print(len(seg["ob"]))
# print(cliprangenow)
nbatch_adapt1 = int((len(seg["ob"]) - nsteps) / nsteps * nbatch_train)
nbatch_adapt2 = int((nsteps) / nsteps * nbatch_train)
# print(rho)
idx1 = []
idx2 = []
kl_rest = np.ones(len(seg["ob"])) * len(seg["ob"]) / nsteps
kl_rest[:-nsteps] = 0
# print(kl_rest)
batch_IS_epoch = np.zeros(noptepochs)
not_use_epoch = np.zeros(noptepochs)
for epo in range(noptepochs):
# Randomize the indexes
# np.random.shuffle(inds)
# 0 to batch_size with batch_train_size step
mean_n, logstd_n = model.meanlogstds(runner.obfilt(seg["ob"]))
# print(np.shape(seg["ac"])[1])
neglogpn = 0.5 * np.sum(np.square((seg["ac"] - mean_n) / np.exp(logstd_n)), axis=-1) \
+ 0.5 * np.log(2.0 * np.pi) * np.shape(seg["ac"])[1] \
+ np.sum(logstd_n, axis=-1)
rho = np.exp(-neglogpn + seg["neglogp"])
batch_rho = np.abs(1.0 - rho) + 1.0
rho_mean = np.mean(batch_rho)
batch_IS_epoch[epo] = rho_mean
# print(nbatch_adapt)
losses_epoch = []
notuse_frac = 0.0
for _ in range(int(nsteps / nbatch_train)):
if nbatch_adapt1 > 0:
idx1 = np.random.choice(inds1, nbatch_adapt1)
idx2 = np.random.choice(inds2, nbatch_adapt2)
# print(np.mean(np.abs(rho[mbinds] - 1.0) + 1.0))
idx = np.hstack([idx1, idx2]).astype(int)
slices = (arr[idx]
for arr in (runner.obfilt(seg["ob"]), ret, gae,
seg["done"], seg["ac"], values[:-1],
seg["neglogp"], seg["mean"],
seg["logstd"], kl_rest, rho))
loss_epoch, notuse_num = model.train(lrnow, cliprangenow,
klconst, rgae, trunc_rho,
*slices)
mblossvals.append(loss_epoch)
losses_epoch.append(loss_epoch)
notuse_frac += notuse_num
notuse_frac /= ((nbatch_adapt1 + nbatch_adapt2) *
(nsteps / nbatch_train))
not_use_epoch[epo] = notuse_frac
batch_IS.append(batch_IS_epoch)
not_use.append(not_use_epoch)
print(batch_IS)
print(not_use)
print(np.shape(batch_IS))
# print(np.mean(losses_epoch, axis=0))
# Feedforward --> get losses --> update
lossvals = np.mean(mblossvals, axis=0)
if adaptive_kl:
print("KL avg :", lossvals[3])
if lossvals[3] > dtarg * 1.5:
klconst *= 2
print("kl const is increased")
elif lossvals[3] < dtarg / 1.5:
klconst /= 2
print("kl const is reduced")
klconst = np.clip(klconst, 2**(-10), 64)
# End timer
tnow = time.time()
# Calculate the fps (frame per second)
fps = int(nbatch / (tnow - tstart))
if update % log_interval == 0 or update == 1:
# Calculates if value function is a good predicator of the returns (ev > 1)
# or if it's just worse than predicting nothing (ev =< 0)
ev = explained_variance(values[:-1], ret)
logger.logkv("batch IS weight",
[int(1000 * s) / 1000. for s in np.array(temp_prior)])
logger.logkv("kl const", klconst)
logger.logkv("clipping factor", cliprangenow)
logger.logkv("serial_timesteps", update * nsteps)
logger.logkv("nupdates", update)
logger.logkv("total_timesteps", update * nbatch)
logger.logkv("fps", fps)
logger.logkv("explained_variance", float(ev))
logger.logkv('eprewmean',
safemean([epinfo['r'] for epinfo in epinfobuf]))
logger.logkv('eplenmean',
safemean([epinfo['l'] for epinfo in epinfobuf]))
if eval_env is not None:
logger.logkv(
'eval_eprewmean',
safemean([epinfo['r'] for epinfo in eval_epinfos]))
logger.logkv(
'eval_eplenmean',
safemean([epinfo['l'] for epinfo in eval_epinfos]))
logger.logkv('time_elapsed', tnow - tfirststart)
for (lossval, lossname) in zip(lossvals, model.loss_names):
logger.logkv(lossname, lossval)
if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
logger.dumpkvs()
if save_interval and (update % save_interval == 0
or update == 1) and logger.get_dir() and (
MPI is None
or MPI.COMM_WORLD.Get_rank() == 0):
checkdir = osp.join(logger.get_dir(), 'checkpoints')
os.makedirs(checkdir, exist_ok=True)
savepath = osp.join(checkdir, '%.5i' % update)
print('Saving to', savepath)
model.save(savepath)
np.savetxt('/home/han/Downloads/log_ppo/graphdata/batchIS_Humanoid.txt',
np.array(batch_IS))
np.savetxt('/home/han/Downloads/log_ppo/graphdata/notuse_Humanoid.txt',
np.array(not_use))
return model
def learn(*,
policy,
env,
nsteps,
total_timesteps,
ent_coef,
lr,
vf_coef=0.5,
max_grad_norm=0.5,
gamma=0.99,
lam=0.95,
log_interval=10,
nminibatches=4,
noptepochs=4,
cliprange=0.2,
save_interval=0,
callback_fn=None):
if isinstance(lr, float): lr = constfn(lr)
else: assert callable(lr)
if isinstance(cliprange, float): cliprange = constfn(cliprange)
else: assert callable(cliprange)
total_timesteps = int(total_timesteps)
nenvs = env.num_envs
ob_space = env.observation_space
ac_space = env.action_space
nbatch = nenvs * nsteps
nbatch_train = nbatch // nminibatches
make_model = lambda: Model(policy=policy,
ob_space=ob_space,
ac_space=ac_space,
nbatch_act=nenvs,
nbatch_train=nbatch_train,
nsteps=nsteps,
ent_coef=ent_coef,
vf_coef=vf_coef,
max_grad_norm=max_grad_norm)
if save_interval and logger.get_dir():
import cloudpickle
with open(osp.join(logger.get_dir(), 'make_model.pkl'), 'wb') as fh:
fh.write(cloudpickle.dumps(make_model))
model = make_model()
runner = Runner(env=env, model=model, nsteps=nsteps, gamma=gamma, lam=lam)
epinfobuf = deque(maxlen=100)
tfirststart = time.time()
nupdates = total_timesteps // nbatch
for update in range(1, nupdates + 1):
if callback_fn is not None:
if not callback_fn(model, update, epinfobuf):
break
assert nbatch % nminibatches == 0
nbatch_train = nbatch // nminibatches
tstart = time.time()
frac = 1.0 - (update - 1.0) / nupdates
lrnow = lr(frac)
cliprangenow = cliprange(frac)
obs, ac_priors, returns, masks, actions, values, neglogpacs, states, epinfos = runner.run(
) #pylint: disable=E0632
epinfobuf.extend(epinfos)
mblossvals = []
if states is None: # nonrecurrent version
inds = np.arange(nbatch)
for _ in range(noptepochs):
np.random.shuffle(inds)
for start in range(0, nbatch, nbatch_train):
end = start + nbatch_train
mbinds = inds[start:end]
slices = (arr[mbinds]
for arr in (obs, ac_priors, returns, masks,
actions, values, neglogpacs))
mblossvals.append(model.train(lrnow, cliprangenow,
*slices))
else: # recurrent version
assert nenvs % nminibatches == 0
envsperbatch = nenvs // nminibatches
envinds = np.arange(nenvs)
flatinds = np.arange(nenvs * nsteps).reshape(nenvs, nsteps)
envsperbatch = nbatch_train // nsteps
for _ in range(noptepochs):
np.random.shuffle(envinds)
for start in range(0, nenvs, envsperbatch):
end = start + envsperbatch
mbenvinds = envinds[start:end]
mbflatinds = flatinds[mbenvinds].ravel()
slices = (arr[mbflatinds]
for arr in (obs, ac_priors, returns, masks,
actions, values, neglogpacs))
mbstates = states[mbenvinds]
mblossvals.append(
model.train(lrnow, cliprangenow, *slices, mbstates))
lossvals = np.mean(mblossvals, axis=0)
tnow = time.time()
fps = int(nbatch / (tnow - tstart))
if update % log_interval == 0 or update == 1:
ev = explained_variance(values, returns)
logger.logkv("serial_timesteps", update * nsteps)
logger.logkv("nupdates", update)
logger.logkv("total_timesteps", update * nbatch)
logger.logkv("fps", fps)
logger.logkv("explained_variance", float(ev))
logger.logkv('eprewmean',
safemean([epinfo['r'] for epinfo in epinfobuf]))
logger.logkv('eplenmean',
safemean([epinfo['l'] for epinfo in epinfobuf]))
logger.logkv('time_elapsed', tnow - tfirststart)
for (lossval, lossname) in zip(lossvals, model.loss_names):
logger.logkv(lossname, lossval)
logger.dumpkvs()
if save_interval and (update % save_interval == 0
or update == 1) and logger.get_dir():
checkdir = osp.join(logger.get_dir(), 'checkpoints')
os.makedirs(checkdir, exist_ok=True)
savepath = osp.join(checkdir, '%.5i' % update)
print('Saving to', savepath)
model.save(savepath)
env.close()
def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2048, ent_coef=0.0, lr=3e-4,
vf_coef=0.5, max_grad_norm=0.5, gamma=0.99, lam=0.95,
log_interval=10, nminibatches=4, noptepochs=4, cliprange=0.2,
save_interval=0, load_path=None, model_fn=None, **network_kwargs):
'''
Learn policy using PPO algorithm (https://arxiv.org/abs/1707.06347)
Parameters:
----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See common/models.py/lstm for more details on using recurrent nets in policies
env: baselines.common.vec_env.VecEnv environment. Needs to be vectorized for parallel environment simulation.
The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class.
nsteps: int number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel)
total_timesteps: int number of timesteps (i.e. number of actions taken in the environment)
ent_coef: float policy entropy coefficient in the optimization objective
lr: float or function learning rate, constant or a schedule function [0,1] -> R+ where 1 is beginning of the
training and 0 is the end of the training.
vf_coef: float value function loss coefficient in the optimization objective
max_grad_norm: float or None gradient norm clipping coefficient
gamma: float discounting factor
lam: float advantage estimation discounting factor (lambda in the paper)
log_interval: int number of timesteps between logging events
nminibatches: int number of training minibatches per update. For recurrent policies,
should be smaller or equal than number of environments run in parallel.
noptepochs: int number of training epochs per update
cliprange: float or function clipping range, constant or schedule function [0,1] -> R+ where 1 is beginning of the training
and 0 is the end of the training
save_interval: int number of timesteps between saving events
load_path: str path to load the model from
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
'''
set_global_seeds(seed)
if isinstance(lr, float): lr = constfn(lr)
else: assert callable(lr)
if isinstance(cliprange, float): cliprange = constfn(cliprange)
else: assert callable(cliprange)
total_timesteps = int(total_timesteps)
policy = build_policy(env, network, **network_kwargs)
# Get the nb of env
nenvs = env.num_envs
# Get state_space and action_space
ob_space = env.observation_space
ac_space = env.action_space
# Calculate the batch_size
nbatch = nenvs * nsteps
nbatch_train = nbatch // nminibatches
# Instantiate the model object (that creates act_model and train_model)
if model_fn is None:
from baselines.ppo2.model import Model
model_fn = Model
model = model_fn(policy=policy, ob_space=ob_space, ac_space=ac_space, nbatch_act=nenvs, nbatch_train=nbatch_train,
nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef,
max_grad_norm=max_grad_norm)
if load_path is not None:
model.load(load_path)
# Instantiate the runner object
runner = Runner(env=env, model=model, nsteps=nsteps, gamma=gamma, lam=lam)
if eval_env is not None:
eval_runner = Runner(env = eval_env, model = model, nsteps = nsteps, gamma = gamma, lam= lam)
epinfobuf = deque(maxlen=100)
if eval_env is not None:
eval_epinfobuf = deque(maxlen=100)
# Start total timer
tfirststart = time.perf_counter()
nupdates = total_timesteps//nbatch
for update in range(1, nupdates+1):
assert nbatch % nminibatches == 0
# Start timer
tstart = time.perf_counter()
frac = 1.0 - (update - 1.0) / nupdates
# Calculate the learning rate
lrnow = lr(frac)
# Calculate the cliprange
cliprangenow = cliprange(frac)
# Get minibatch
obs, returns, masks, actions, values, neglogpacs, states, epinfos = runner.run() #pylint: disable=E0632
if eval_env is not None:
eval_obs, eval_returns, eval_masks, eval_actions, eval_values, eval_neglogpacs, eval_states, eval_epinfos = eval_runner.run() #pylint: disable=E0632
epinfobuf.extend(epinfos)
if eval_env is not None:
eval_epinfobuf.extend(eval_epinfos)
# Here what we're going to do is for each minibatch calculate the loss and append it.
mblossvals = []
if states is None: # nonrecurrent version
# Index of each element of batch_size
# Create the indices array
inds = np.arange(nbatch)
for _ in range(noptepochs):
# Randomize the indexes
np.random.shuffle(inds)
# 0 to batch_size with batch_train_size step
for start in range(0, nbatch, nbatch_train):
end = start + nbatch_train
mbinds = inds[start:end]
slices = (arr[mbinds] for arr in (obs, returns, masks, actions, values, neglogpacs))
mblossvals.append(model.train(lrnow, cliprangenow, *slices))
else: # recurrent version
assert nenvs % nminibatches == 0
envsperbatch = nenvs // nminibatches
envinds = np.arange(nenvs)
flatinds = np.arange(nenvs * nsteps).reshape(nenvs, nsteps)
for _ in range(noptepochs):
np.random.shuffle(envinds)
for start in range(0, nenvs, envsperbatch):
end = start + envsperbatch
mbenvinds = envinds[start:end]
mbflatinds = flatinds[mbenvinds].ravel()
slices = (arr[mbflatinds] for arr in (obs, returns, masks, actions, values, neglogpacs))
mbstates = states[mbenvinds]
mblossvals.append(model.train(lrnow, cliprangenow, *slices, mbstates))
# Feedforward --> get losses --> update
lossvals = np.mean(mblossvals, axis=0)
# End timer
tnow = time.perf_counter()
# Calculate the fps (frame per second)
fps = int(nbatch / (tnow - tstart))
if update % log_interval == 0 or update == 1:
# Calculates if value function is a good predicator of the returns (ev > 1)
# or if it's just worse than predicting nothing (ev =< 0)
ev = explained_variance(values, returns)
logger.logkv("serial_timesteps", update*nsteps)
logger.logkv("nupdates", update)
logger.logkv("total_timesteps", update*nbatch)
logger.logkv("fps", fps)
logger.logkv("explained_variance", float(ev))
logger.logkv('eprewmean', safemean([epinfo['r'] for epinfo in epinfobuf]))
logger.logkv('eplenmean', safemean([epinfo['l'] for epinfo in epinfobuf]))
if eval_env is not None:
logger.logkv('eval_eprewmean', safemean([epinfo['r'] for epinfo in eval_epinfobuf]) )
logger.logkv('eval_eplenmean', safemean([epinfo['l'] for epinfo in eval_epinfobuf]) )
logger.logkv('time_elapsed', tnow - tfirststart)
for (lossval, lossname) in zip(lossvals, model.loss_names):
logger.logkv(lossname, lossval)
if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
logger.dumpkvs()
if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir() and (MPI is None or MPI.COMM_WORLD.Get_rank() == 0):
checkdir = osp.join(logger.get_dir(), 'checkpoints')
os.makedirs(checkdir, exist_ok=True)
savepath = osp.join(checkdir, '%.5i'%update)
print('Saving to', savepath)
model.save(savepath)
return model
def learn(
env,
policy_fn,
reward_giver,
expert_dataset,
*,
timesteps_per_actorbatch, # timesteps per actor per update
clip_param,
entcoeff, # clipping parameter epsilon, entropy coeff
optim_epochs,
optim_stepsize,
optim_batchsize, # optimization hypers
gamma,
lam, # advantage estimation
max_timesteps=0,
max_episodes=0,
max_iters=0,
max_seconds=0, # time constraint
callback=None, # you can do anything in the callback, since it takes locals(), globals()
adam_epsilon=1e-5,
schedule='constant' # annealing for stepsize parameters (epsilon and adam)
):
# Setup losses and stuff
# ----------------------------------------
d_stepsize = 3e-4
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_fn("pi", ob_space,
ac_space) # Construct network for new policy
oldpi = policy_fn("oldpi", ob_space, ac_space) # Network for old policy
atarg = tf.placeholder(
dtype=tf.float32,
shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
lrmult = tf.placeholder(
name='lrmult', dtype=tf.float32,
shape=[]) # learning rate multiplier, updated with schedule
clip_param = clip_param * lrmult # Annealed cliping parameter epislon
ob = U.get_placeholder_cached(name="ob")
ac = pi.pdtype.sample_placeholder([None])
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
pol_entpen = (-entcoeff) * meanent
ratio = tf.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac)) # pnew / pold
surr1 = ratio * atarg # surrogate from conservative policy iteration
surr2 = tf.clip_by_value(ratio, 1.0 - clip_param,
1.0 + clip_param) * atarg #
pol_surr = -tf.reduce_mean(tf.minimum(
surr1, surr2)) # PPO's pessimistic surrogate (L^CLIP)
vf_loss = tf.reduce_mean(tf.square(pi.vpred - ret))
total_loss = pol_surr + pol_entpen + vf_loss
losses = [pol_surr, pol_entpen, vf_loss, meankl, meanent]
loss_names = ["pol_surr", "pol_entpen", "vf_loss", "kl", "ent"]
var_list = pi.get_trainable_variables()
lossandgrad = U.function([ob, ac, atarg, ret, lrmult],
losses + [U.flatgrad(total_loss, var_list)])
adam = MpiAdam(var_list, epsilon=adam_epsilon)
d_adam = MpiAdam(reward_giver.get_trainable_variables())
assign_old_eq_new = U.function(
[], [],
updates=[
tf.assign(oldv, newv)
for (oldv,
newv) in zipsame(oldpi.get_variables(), pi.get_variables())
])
compute_losses = U.function([ob, ac, atarg, ret, lrmult], losses)
U.initialize()
adam.sync()
d_adam.sync()
# Prepare for rollouts
# ----------------------------------------
viewer = mujoco_py.MjViewer(env.sim)
seg_gen = traj_segment_generator(pi,
env,
viewer,
reward_giver,
timesteps_per_actorbatch,
stochastic=True)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=40) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=40) # rolling buffer for episode rewards
true_rewbuffer = deque(maxlen=40)
assert sum(
[max_iters > 0, max_timesteps > 0, max_episodes > 0,
max_seconds > 0]) == 1, "Only one time constraint permitted"
while True:
if callback: callback(locals(), globals())
if max_timesteps and timesteps_so_far >= max_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
elif max_seconds and time.time() - tstart >= max_seconds:
break
if schedule == 'constant':
cur_lrmult = 1.0
elif schedule == 'linear':
cur_lrmult = max(1.0 - float(timesteps_so_far) / max_timesteps, 0)
else:
raise NotImplementedError
logger.log("********** Iteration %i ************" % iters_so_far)
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, gamma, lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg[
"tdlamret"]
vpredbefore = seg["vpred"] # predicted value function before udpate
atarg = (atarg - atarg.mean()
) / atarg.std() # standardized advantage function estimate
d = Dataset(dict(ob=ob, ac=ac, atarg=atarg, vtarg=tdlamret),
shuffle=not pi.recurrent)
optim_batchsize = optim_batchsize or ob.shape[0]
if hasattr(pi, "ob_rms"):
pi.ob_rms.update(ob) # update running mean/std for policy
assign_old_eq_new() # set old parameter values to new parameter values
logger.log("Optimizing...")
logger.log(fmt_row(13, loss_names))
# Here we do a bunch of optimization epochs over the data
for _ in range(optim_epochs):
losses = [
] # list of tuples, each of which gives the loss for a minibatch
for batch in d.iterate_once(optim_batchsize):
*newlosses, g = lossandgrad(batch["ob"], batch["ac"],
batch["atarg"], batch["vtarg"],
cur_lrmult)
adam.update(g, optim_stepsize * cur_lrmult)
losses.append(newlosses)
logger.log(fmt_row(13, np.mean(losses, axis=0)))
# ------------------ Update D ------------------
logger.log("Optimizing Discriminator...")
logger.log(fmt_row(13, reward_giver.loss_name))
ob_expert, ac_expert = expert_dataset.get_next_batch(len(ob))
batch_size = len(ob)
d_losses = [
] # list of tuples, each of which gives the loss for a minibatch
for ob_batch, ac_batch in dataset.iterbatches(
(ob, ac), include_final_partial_batch=False,
batch_size=batch_size):
ob_expert, ac_expert = expert_dataset.get_next_batch(len(ob_batch))
# update running mean/std for reward_giver
if hasattr(reward_giver, "obs_rms"):
reward_giver.obs_rms.update(
np.concatenate((ob_batch, ob_expert), 0))
*newlosses, g = reward_giver.lossandgrad(ob_batch, ac_batch,
ob_expert, ac_expert)
d_adam.update(g, d_stepsize)
d_losses.append(newlosses)
logger.log("Evaluating losses...")
losses = []
for batch in d.iterate_once(optim_batchsize):
newlosses = compute_losses(batch["ob"], batch["ac"],
batch["atarg"], batch["vtarg"],
cur_lrmult)
losses.append(newlosses)
meanlosses, _, _ = mpi_moments(losses, axis=0)
logger.log(fmt_row(13, meanlosses))
for (lossval, name) in zipsame(meanlosses, loss_names):
logger.record_tabular("loss_" + name, lossval)
logger.record_tabular("ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
if MPI.COMM_WORLD.Get_rank() == 0:
logger.dump_tabular()
return pi
def learn(
*,
network,
env,
save,
total_timesteps,
timesteps_per_batch=1024, # what to train on
max_kl=0.001,
cg_iters=10,
gamma=0.99,
lam=1.0, # advantage estimation
seed=None,
ent_coef=0.0,
cg_damping=1e-2,
vf_stepsize=3e-4,
vf_iters=3,
max_episodes=0,
max_iters=0, # ttotal_timestepsime constraint
callback=None,
load_path=None,
**network_kwargs):
'''
learn a policy function with TRPO algorithm
Parameters:
----------
network neural network to learn. Can be either string ('mlp', 'cnn', 'lstm', 'lnlstm' for basic types)
or function that takes input placeholder and returns tuple (output, None) for feedforward nets
or (output, (state_placeholder, state_output, mask_placeholder)) for recurrent nets
env environment (one of the gym environments or wrapped via baselines.common.vec_env.VecEnv-type class
timesteps_per_batch timesteps per gradient estimation batch
max_kl max KL divergence between old policy and new policy ( KL(pi_old || pi) )
ent_coef coefficient of policy entropy term in the optimization objective
cg_iters number of iterations of conjugate gradient algorithm
cg_damping conjugate gradient damping
vf_stepsize learning rate for adam optimizer used to optimie value function loss
vf_iters number of iterations of value function optimization iterations per each policy optimization step
total_timesteps max number of timesteps
max_episodes max number of episodes
max_iters maximum number of policy optimization iterations
callback function to be called with (locals(), globals()) each policy optimization step
load_path str, path to load the model from (default: None, i.e. no model is loaded)
**network_kwargs keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
Returns:
-------
learnt model
'''
if MPI is not None:
nworkers = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
else:
nworkers = 1
rank = 0
set_global_seeds(seed)
np.set_printoptions(precision=3)
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
if isinstance(network, str):
network, network_model = get_network_builder(network)(**network_kwargs)
with tf.name_scope("pi"):
pi_policy_network = network(ob_space.shape)
pi_value_network = network(ob_space.shape)
pi = PolicyWithValue(ac_space, pi_policy_network, pi_value_network)
with tf.name_scope("oldpi"):
old_pi_policy_network = network(ob_space.shape)
old_pi_value_network = network(ob_space.shape)
oldpi = PolicyWithValue(ac_space, old_pi_policy_network,
old_pi_value_network)
pi_var_list = pi_policy_network.trainable_variables + list(
pi.pdtype.trainable_variables)
old_pi_var_list = old_pi_policy_network.trainable_variables + list(
oldpi.pdtype.trainable_variables)
vf_var_list = pi_value_network.trainable_variables + pi.value_fc.trainable_variables
old_vf_var_list = old_pi_value_network.trainable_variables + oldpi.value_fc.trainable_variables
if load_path is not None:
load_path = osp.expanduser(load_path)
ckpt = tf.train.Checkpoint(model=pi)
manager = tf.train.CheckpointManager(ckpt, load_path, max_to_keep=None)
ckpt.restore(manager.latest_checkpoint)
vfadam = MpiAdam(vf_var_list)
get_flat = U.GetFlat(pi_var_list)
set_from_flat = U.SetFromFlat(pi_var_list)
loss_names = ["optimgain", "meankl", "entloss", "surrgain", "entropy"]
shapes = [var.get_shape().as_list() for var in pi_var_list]
def assign_old_eq_new():
for pi_var, old_pi_var in zip(pi_var_list, old_pi_var_list):
old_pi_var.assign(pi_var)
for vf_var, old_vf_var in zip(vf_var_list, old_vf_var_list):
old_vf_var.assign(vf_var)
@tf.function
def compute_lossandgrad(ob, ac, atarg):
with tf.GradientTape() as tape:
old_policy_latent = oldpi.policy_network(ob)
old_pd, _ = oldpi.pdtype.pdfromlatent(old_policy_latent)
policy_latent = pi.policy_network(ob)
pd, _ = pi.pdtype.pdfromlatent(policy_latent)
kloldnew = old_pd.kl(pd)
ent = pd.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
entbonus = ent_coef * meanent
ratio = tf.exp(pd.logp(ac) - old_pd.logp(ac))
surrgain = tf.reduce_mean(ratio * atarg)
optimgain = surrgain + entbonus
losses = [optimgain, meankl, entbonus, surrgain, meanent]
gradients = tape.gradient(optimgain, pi_var_list)
return losses + [U.flatgrad(gradients, pi_var_list)]
@tf.function
def compute_losses(ob, ac, atarg):
old_policy_latent = oldpi.policy_network(ob)
old_pd, _ = oldpi.pdtype.pdfromlatent(old_policy_latent)
policy_latent = pi.policy_network(ob)
pd, _ = pi.pdtype.pdfromlatent(policy_latent)
kloldnew = old_pd.kl(pd)
ent = pd.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
entbonus = ent_coef * meanent
ratio = tf.exp(pd.logp(ac) - old_pd.logp(ac))
surrgain = tf.reduce_mean(ratio * atarg)
optimgain = surrgain + entbonus
losses = [optimgain, meankl, entbonus, surrgain, meanent]
return losses
#ob shape should be [batch_size, ob_dim], merged nenv
#ret shape should be [batch_size]
@tf.function
def compute_vflossandgrad(ob, ret):
with tf.GradientTape() as tape:
pi_vf = pi.value(ob)
vferr = tf.reduce_mean(tf.square(pi_vf - ret))
return U.flatgrad(tape.gradient(vferr, vf_var_list), vf_var_list)
@tf.function
def compute_fvp(flat_tangent, ob, ac, atarg):
with tf.GradientTape() as outter_tape:
with tf.GradientTape() as inner_tape:
old_policy_latent = oldpi.policy_network(ob)
old_pd, _ = oldpi.pdtype.pdfromlatent(old_policy_latent)
policy_latent = pi.policy_network(ob)
pd, _ = pi.pdtype.pdfromlatent(policy_latent)
kloldnew = old_pd.kl(pd)
meankl = tf.reduce_mean(kloldnew)
klgrads = inner_tape.gradient(meankl, pi_var_list)
start = 0
tangents = []
for shape in shapes:
sz = U.intprod(shape)
tangents.append(
tf.reshape(flat_tangent[start:start + sz], shape))
start += sz
gvp = tf.add_n([
tf.reduce_sum(g * tangent)
for (g, tangent) in zipsame(klgrads, tangents)
])
hessians_products = outter_tape.gradient(gvp, pi_var_list)
fvp = U.flatgrad(hessians_products, pi_var_list)
return fvp
@contextmanager
def timed(msg):
if rank == 0:
print(colorize(msg, color='magenta'))
tstart = time.time()
yield
print(
colorize("done in %.3f seconds" % (time.time() - tstart),
color='magenta'))
else:
yield
def allmean(x):
assert isinstance(x, np.ndarray)
if MPI is not None:
out = np.empty_like(x)
MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
out /= nworkers
else:
out = np.copy(x)
return out
th_init = get_flat()
if MPI is not None:
MPI.COMM_WORLD.Bcast(th_init, root=0)
set_from_flat(th_init)
vfadam.sync()
print("Init param sum", th_init.sum(), flush=True)
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi, env, timesteps_per_batch)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=40) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=40) # rolling buffer for episode rewards
# ---------------------- New ----------------------
rewforbuffer = deque(maxlen=40)
rewctrlbuffer = deque(maxlen=40)
rewconbuffer = deque(maxlen=40)
rewsurbuffer = deque(maxlen=40)
rewformeanbuf = np.array([])
rewctrlmeanbuf = np.array([])
rewconmeanbuf = np.array([])
rewsurmeanbuf = np.array([])
# -------------------------------------------------
if sum([max_iters > 0, total_timesteps > 0, max_episodes > 0]) == 0:
# nothing to be done
return pi
assert sum([max_iters>0, total_timesteps>0, max_episodes>0]) < 2, \
'out of max_iters, total_timesteps, and max_episodes only one should be specified'
x_axis = 0
x_holder = np.array([])
rew_holder = np.array([])
while True:
if timesteps_so_far > total_timesteps - 1500: #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# Set recording XXXX timesteps before ending
env = VecVideoRecorder(env,
osp.join(logger.get_dir(), "videos"),
record_video_trigger=lambda x: True,
video_length=200)
seg_gen = traj_segment_generator(pi, env, timesteps_per_batch)
if callback: callback(locals(), globals())
if total_timesteps and timesteps_so_far >= total_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
logger.log("********** Iteration %i ************" % iters_so_far)
with timed("sampling"):
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, gamma, lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg[
"tdlamret"]
ob = sf01(ob)
vpredbefore = seg["vpred"] # predicted value function before udpate
atarg = (atarg - atarg.mean()
) / atarg.std() # standardized advantage function estimate
if hasattr(pi, "ret_rms"): pi.ret_rms.update(tdlamret)
if hasattr(pi, "ob_rms"):
pi.ob_rms.update(ob) # update running mean/std for policy
args = ob, ac, atarg
fvpargs = [arr[::5] for arr in args]
def fisher_vector_product(p):
return allmean(compute_fvp(p, *fvpargs).numpy()) + cg_damping * p
assign_old_eq_new() # set old parameter values to new parameter values
with timed("computegrad"):
*lossbefore, g = compute_lossandgrad(*args)
lossbefore = allmean(np.array(lossbefore))
g = g.numpy()
g = allmean(g)
if np.allclose(g, 0):
logger.log("Got zero gradient. not updating")
else:
with timed("cg"):
stepdir = cg(fisher_vector_product,
g,
cg_iters=cg_iters,
verbose=rank == 0)
assert np.isfinite(stepdir).all()
shs = .5 * stepdir.dot(fisher_vector_product(stepdir))
lm = np.sqrt(shs / max_kl)
# logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g))
fullstep = stepdir / lm
expectedimprove = g.dot(fullstep)
surrbefore = lossbefore[0]
stepsize = 1.0
thbefore = get_flat()
for _ in range(10):
thnew = thbefore + fullstep * stepsize
set_from_flat(thnew)
meanlosses = surr, kl, *_ = allmean(
np.array(compute_losses(*args)))
improve = surr - surrbefore
logger.log("Expected: %.3f Actual: %.3f" %
(expectedimprove, improve))
if not np.isfinite(meanlosses).all():
logger.log("Got non-finite value of losses -- bad!")
elif kl > max_kl * 1.5:
logger.log("violated KL constraint. shrinking step.")
elif improve < 0:
logger.log("surrogate didn't improve. shrinking step.")
else:
logger.log("Stepsize OK!")
break
stepsize *= .5
else:
logger.log("couldn't compute a good step")
set_from_flat(thbefore)
if nworkers > 1 and iters_so_far % 20 == 0:
paramsums = MPI.COMM_WORLD.allgather(
(thnew.sum(), vfadam.getflat().sum())) # list of tuples
assert all(
np.allclose(ps, paramsums[0]) for ps in paramsums[1:])
for (lossname, lossval) in zip(loss_names, meanlosses):
logger.record_tabular(lossname, lossval)
with timed("vf"):
for _ in range(vf_iters):
for (mbob, mbret) in dataset.iterbatches(
(seg["ob"], seg["tdlamret"]),
include_final_partial_batch=False,
batch_size=64):
mbob = sf01(mbob)
g = allmean(compute_vflossandgrad(mbob, mbret).numpy())
vfadam.update(g, vf_stepsize)
logger.record_tabular("ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
lrlocal = (seg["ep_lens"], seg["ep_rets"], seg["ep_rets_for"],
seg["ep_rets_ctrl"], seg["ep_rets_con"], seg["ep_rets_sur"]
) # local values
if MPI is not None:
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
else:
listoflrpairs = [lrlocal]
lens, rews, rews_for, rews_ctrl, rews_con, rews_sur = map(
flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
# ---------------------- New ----------------------
rewforbuffer.extend(rews_for)
rewctrlbuffer.extend(rews_ctrl)
rewconbuffer.extend(rews_con)
rewsurbuffer.extend(rews_sur)
rewformeanbuf = np.append([rewformeanbuf], [np.mean(rewforbuffer)])
rewctrlmeanbuf = np.append([rewctrlmeanbuf], [np.mean(rewctrlbuffer)])
rewconmeanbuf = np.append([rewconmeanbuf], [np.mean(rewconbuffer)])
rewsurmeanbuf = np.append([rewsurmeanbuf], [np.mean(rewsurbuffer)])
# -------------------------------------------------
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
if rank == 0:
logger.dump_tabular()
x_axis += 1
x_holder = np.append([x_holder], [x_axis])
rew_holder = np.append([rew_holder], [np.mean(rewbuffer)])
# --------------------------------------- NEW -----------------------------------------------------
with open("img_rec.txt", "r") as rec:
cur_gen = rec.read()
cur_gen = cur_gen.strip() # remove \n
dir_of_gens = [
'1_1', '2_1', '3_1', '1_2', '2_2', '3_2', '1_3', '2_3', '3_3', '1_4',
'2_4', '3_4', '1_5', '2_5', '3_5', '1_6', '2_6', '3_6', '1_7', '2_7',
'3_7', '1_8', '2_8', '3_8', '1_9', '2_9', '3_9', '1_10', '2_10',
'3_10', '1_11', '2_11', '3_11', '1_12', '2_12', '3_12'
]
# -------------------------------------------------------------------------------------------------
from matplotlib import pyplot as plt
f = plt.figure(1)
plt.plot(x_holder, rew_holder)
plt.title("Rewards for Ant v2")
plt.grid(True)
plt.savefig('rewards_for_antv2_{}'.format(cur_gen))
g = plt.figure(2)
plt.plot(x_holder, rewformeanbuf, label='Forward Reward')
plt.plot(x_holder, rewctrlmeanbuf, label='CTRL Cost')
plt.plot(x_holder, rewconmeanbuf, label='Contact Cost')
plt.plot(x_holder, rewsurmeanbuf, label='Survive Reward')
plt.title("Reward Breakdown")
plt.legend()
plt.grid(True)
plt.savefig('rewards_breakdown{}'.format(cur_gen))
# plt.show()
# --------------------------------------- NEW -----------------------------------------------------
elem = int(dir_of_gens.index(cur_gen))
with open("img_rec.txt", "w") as rec:
if elem == 35:
new_elem = 0
else:
new_elem = elem + 1
new_gen = cur_gen.replace(cur_gen, dir_of_gens[new_elem])
rec.write(new_gen)
# -------------------------------------------------------------------------------------------------
#----------------------------------------------------------- SAVE WEIGHTS ------------------------------------------------------------#
# np.save('val_weights_bias_2_c',val_weights_bias_2_c) # <-------------------------------------------------------------------------------------
# save = save.replace(save[0],'..',2)
# os.chdir(save)
# name = 'max_reward'
# completeName = os.path.join(name+".txt")
# file1 = open(completeName,"w")
# toFile = str(np.mean(rewbuffer))
# file1.write(toFile)
# file1.close()
# os.chdir('../../../baselines-tf2')
return pi
def learn(env, policy_fn, *,
timesteps_per_actorbatch, # timesteps per actor per update
clip_param, entcoeff, # clipping parameter epsilon, entropy coeff
optim_epochs, optim_stepsize, optim_batchsize, # optimization hypers
gamma, lam, # advantage estimation
max_timesteps=0, max_episodes=0, max_iters=0, max_seconds=0, # time constraint
callback=None, # you can do anything in the callback, since it takes locals(), globals()
adam_epsilon=1e-5,
schedule='constant', # annealing for stepsize parameters (epsilon and adam)
**kwargs,
):
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_fn("pi", ob_space, ac_space) # Construct network for new policy
oldpi = policy_fn("oldpi", ob_space, ac_space) # Network for old policy
atarg = tf.placeholder(dtype=tf.float32, shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
atarg_novel = tf.placeholder(dtype=tf.float32,
shape=[None]) # Target advantage function for the novelty reward term
ret_novel = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return for the novelty reward term
lrmult = tf.placeholder(name='lrmult', dtype=tf.float32,
shape=[]) # learning rate multiplier, updated with schedule
clip_param = clip_param * lrmult # Annealed cliping parameter epislon
ob = U.get_placeholder_cached(name="ob")
ac = pi.pdtype.sample_placeholder([None])
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
pol_entpen = (-entcoeff) * meanent
ratio = tf.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac)) # pnew / pold
surr1 = ratio * atarg # surrogate from conservative policy iteration
surr2 = tf.clip_by_value(ratio, 1.0 - clip_param, 1.0 + clip_param) * atarg #
surr1_novel = ratio * atarg_novel # surrogate loss of the novelty term
surr2_novel = tf.clip_by_value(ratio, 1.0 - clip_param,
1.0 + clip_param) * atarg_novel # surrogate loss of the novelty term
pol_surr = - tf.reduce_mean(tf.minimum(surr1, surr2)) # PPO's pessimistic surrogate (L^CLIP)
pol_surr_novel = -tf.reduce_mean(tf.minimum(surr1_novel, surr2_novel)) # PPO's surrogate for the novelty part
vf_loss = tf.reduce_mean(tf.square(pi.vpred - ret))
vf_loss_novel = tf.reduce_mean(tf.square(pi.vpred_novel - ret_novel))
total_loss = pol_surr + pol_entpen + vf_loss
losses = [pol_surr, pol_entpen, vf_loss, meankl, meanent]
total_loss_novel = pol_surr_novel + pol_entpen + vf_loss_novel
losses_novel = [pol_surr_novel, pol_entpen, vf_loss_novel, meankl, meanent]
loss_names = ["pol_surr", "pol_entpen", "vf_loss", "kl", "ent"]
policy_var_list = pi.get_trainable_variables(scope='pi/pol')
policy_var_count = 0
for vars in policy_var_list:
count_in_var = 1
for dim in vars.shape._dims:
count_in_var *= dim
policy_var_count += count_in_var
noise_count = pi.get_trainable_variables(scope='pi/pol/logstd')[0].shape._dims[1]
var_list = pi.get_trainable_variables(scope='pi/pol') + pi.get_trainable_variables(scope='pi/vf/')
var_list_novel = pi.get_trainable_variables(scope='pi/pol') + pi.get_trainable_variables(scope='pi/vf_novel/')
lossandgrad = U.function([ob, ac, atarg, ret, lrmult], losses + [U.flatgrad(total_loss, var_list)])
lossandgrad_novel = U.function([ob, ac, atarg_novel, ret_novel, lrmult],
losses_novel + [U.flatgrad(total_loss_novel, var_list_novel)])
adam = MpiAdam(var_list, epsilon=adam_epsilon)
adam_novel = MpiAdam(var_list_novel, epsilon=adam_epsilon)
assign_old_eq_new = U.function([], [], updates=[tf.assign(oldv, newv)
for (oldv, newv) in
zipsame(oldpi.get_variables(), pi.get_variables())])
compute_losses = U.function([ob, ac, atarg, ret, lrmult], losses)
compute_losses_novel = U.function([ob, ac, atarg_novel, ret_novel, lrmult], losses_novel)
U.initialize()
adam.sync()
adam_novel.sync()
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi, env, timesteps_per_actorbatch, stochastic=True)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
novelty_update_iter_cycle = 10
novelty_start_iter = 50
novelty_update = True
tstart = time.time()
lenbuffer = deque(maxlen=100) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=100) # rolling buffer for episode rewards
rewnovelbuffer = deque(maxlen=100) # rolling buffer for episode novelty rewards
assert sum([max_iters > 0, max_timesteps > 0, max_episodes > 0,
max_seconds > 0]) == 1, "Only one time constraint permitted"
while True:
if callback: callback(locals(), globals())
if max_timesteps and timesteps_so_far >= max_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
elif max_seconds and time.time() - tstart >= max_seconds:
break
if schedule == 'constant':
cur_lrmult = 1.0
elif schedule == 'linear':
cur_lrmult = max(1.0 - float(timesteps_so_far) / max_timesteps, 0)
else:
raise NotImplementedError
logger.log("********** Iteration %i ************" % iters_so_far)
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, gamma, lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
ob, ac, atarg, atarg_novel, tdlamret, tdlamret_novel = seg["ob"], seg["ac"], seg["adv"], seg["adv_novel"], seg[
"tdlamret"], seg["tdlamret_novel"]
vpredbefore = seg["vpred"] # predicted value function before udpate
vprednovelbefore = seg['vpred_novel'] # predicted novelty value function before update
atarg = (atarg - atarg.mean()) / atarg.std() # standardized advantage function estimate
atarg_novel = (
atarg_novel - atarg_novel.mean()) / atarg_novel.std() # standartized novelty advantage function estimate
d = Dataset(
dict(ob=ob, ac=ac, atarg=atarg, vtarg=tdlamret, atarg_novel=atarg_novel, vtarg_novel=tdlamret_novel),
shuffle=not pi.recurrent)
optim_batchsize = optim_batchsize or ob.shape[0]
if hasattr(pi, "ob_rms"): pi.ob_rms.update(ob) # update running mean/std for policy
assign_old_eq_new() # set old parameter values to new parameter values
logger.log("Optimizing...")
logger.log(fmt_row(13, loss_names))
task_gradient_mag = [0]
# Here we do a bunch of optimization epochs over the data
for _ in range(optim_epochs):
losses = [] # list of tuples, each of which gives the loss for a minibatch
for batch in d.iterate_once(optim_batchsize):
*newlosses, g = lossandgrad(batch["ob"], batch["ac"], batch["atarg"], batch["vtarg"], cur_lrmult)
adam.update(g, optim_stepsize * cur_lrmult)
# adam_novel.update(g_novel, optim_stepsize * cur_lrmult)
losses.append(newlosses)
logger.log(fmt_row(13, np.mean(losses, axis=0)))
logger.log("Evaluating losses...")
losses = []
for batch in d.iterate_once(optim_batchsize):
newlosses = compute_losses(batch["ob"], batch["ac"], batch["atarg"], batch["vtarg"], cur_lrmult)
# newlosses_novel = compute_losses_novel(batch["ob"], batch["ac"], batch["atarg_novel"], batch["vtarg_novel"],
# cur_lrmult)
losses.append(newlosses)
meanlosses, _, _ = mpi_moments(losses, axis=0)
logger.log(fmt_row(13, meanlosses))
for (lossval, name) in zipsame(meanlosses, loss_names):
logger.record_tabular("loss_" + name, lossval)
logger.record_tabular("ev_tdlam_before", explained_variance(vpredbefore, tdlamret))
lrlocal = (seg["ep_lens"], seg["ep_rets"], seg['ep_rets_novel']) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews, rews_novel = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
rewnovelbuffer.extend(rews_novel)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpRNoveltyRewMean", np.mean(rewnovelbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
if iters_so_far >= novelty_start_iter and iters_so_far % novelty_update_iter_cycle == 0:
novelty_update = not novelty_update
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
logger.record_tabular("TaskGradMag", np.array(task_gradient_mag).mean())
# logger.record_tabular("NoveltyUpdate", novelty_update)
if MPI.COMM_WORLD.Get_rank() == 0:
logger.dump_tabular()
return pi
def learn(network, env, seed, total_timesteps=int(40e6), gamma=0.99, log_interval=1, nprocs=32, nsteps=20,
ent_coef=0.01, vf_coef=0.5, vf_fisher_coef=1.0, lr=0.25, max_grad_norm=0.5,
kfac_clip=0.001, save_interval=None, lrschedule='linear', load_path=None, is_async=True, **network_kwargs):
set_global_seeds(seed)
if network == 'cnn':
network_kwargs['one_dim_bias'] = True
policy = build_policy(env, network, **network_kwargs)
nenvs = env.num_envs
ob_space = env.observation_space
ac_space = env.action_space
make_model = lambda : Model(policy, ob_space, ac_space, nenvs, total_timesteps, nprocs=nprocs, nsteps
=nsteps, ent_coef=ent_coef, vf_coef=vf_coef, vf_fisher_coef=
vf_fisher_coef, lr=lr, max_grad_norm=max_grad_norm, kfac_clip=kfac_clip,
lrschedule=lrschedule, is_async=is_async)
if save_interval and logger.get_dir():
import cloudpickle
with open(osp.join(logger.get_dir(), 'make_model.pkl'), 'wb') as fh:
fh.write(cloudpickle.dumps(make_model))
model = make_model()
if load_path is not None:
model.load(load_path)
runner = Runner(env, model, nsteps=nsteps, gamma=gamma)
epinfobuf = deque(maxlen=100)
nbatch = nenvs*nsteps
tstart = time.time()
coord = tf.train.Coordinator()
if is_async:
enqueue_threads = model.q_runner.create_threads(model.sess, coord=coord, start=True)
else:
enqueue_threads = []
for update in range(1, total_timesteps//nbatch+1):
obs, states, rewards, masks, actions, values, epinfos = runner.run()
epinfobuf.extend(epinfos)
policy_loss, value_loss, policy_entropy = model.train(obs, states, rewards, masks, actions, values)
model.old_obs = obs
nseconds = time.time()-tstart
fps = int((update*nbatch)/nseconds)
if update % log_interval == 0 or update == 1:
ev = explained_variance(values, rewards)
logger.record_tabular("nupdates", update)
logger.record_tabular("total_timesteps", update*nbatch)
logger.record_tabular("fps", fps)
logger.record_tabular("policy_entropy", float(policy_entropy))
logger.record_tabular("policy_loss", float(policy_loss))
logger.record_tabular("value_loss", float(value_loss))
logger.record_tabular("explained_variance", float(ev))
logger.record_tabular("eprewmean", safemean([epinfo['r'] for epinfo in epinfobuf]))
logger.record_tabular("eplenmean", safemean([epinfo['l'] for epinfo in epinfobuf]))
logger.dump_tabular()
if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir():
savepath = osp.join(logger.get_dir(), 'checkpoint%.5i'%update)
print('Saving to', savepath)
model.save(savepath)
coord.request_stop()
coord.join(enqueue_threads)
return model
def learn(*,
env,
nsteps,
total_timesteps,
ent_coef,
lr,
nmap_args,
vf_coef=0.5,
max_grad_norm=0.5,
gamma=0.99,
lam=0.95,
nminibatches=4,
noptepochs=1,
cliprange=0.2,
log_interval=1,
save_interval=10,
load=None):
if isinstance(lr, float): lr = constfn(lr)
else: assert callable(lr)
if isinstance(cliprange, float): cliprange = constfn(cliprange)
else: assert callable(cliprange)
total_timesteps = int(total_timesteps)
nenvs = env.num_envs
ob_space = env.observation_space
ac_space = env.action_space
nbatch = nenvs * nsteps
nbatch_train = nbatch // nminibatches
make_model = lambda: NeuralMapModel(ob_space=ob_space,
ac_space=ac_space,
nbatch_act=nenvs,
nbatch_train=nbatch_train,
nsteps=nsteps,
ent_coef=ent_coef,
vf_coef=vf_coef,
nmap_args=nmap_args,
max_grad_norm=max_grad_norm)
model = make_model()
runner = Runner(env=env, model=model, nsteps=nsteps, gamma=gamma, lam=lam)
if load == None:
print("""Will save in %s""" % nmap_args['savepath'])
#if save_interval and nmap_args['savepath']:
#import cloudpickle
#with open(osp.join(nmap_args['savepath'], 'make_model.pkl'), 'wb') as fh:
#fh.write(cloudpickle.dumps(make_model))
epinfobuf = deque(maxlen=100)
tfirststart = time.time()
nupdates = total_timesteps // nbatch
for update in range(1, nupdates + 1):
print('UPDATE NUMBER = ', update)
assert nbatch % nminibatches == 0
nbatch_train = nbatch // nminibatches
tstart = time.time()
frac = 1.0 - (update - 1.0) / nupdates
lrnow = lr(frac)
cliprangenow = cliprange(frac)
obs, returns, masks, actions, values, neglogpacs, states, epinfos = runner.run(
) #pylint: disable=E0632
epinfobuf.extend(epinfos)
mblossvals = []
if states is None: # nonrecurrent version
inds = np.arange(nbatch)
for _ in range(noptepochs):
np.random.shuffle(inds)
for start in range(0, nbatch, nbatch_train):
end = start + nbatch_train
mbinds = inds[start:end]
slices = (arr[mbinds]
for arr in (obs, returns, masks, actions,
values, neglogpacs))
mblossvals.append(
model.train(lrnow, cliprangenow, *slices))
else: # recurrent version
assert nenvs % nminibatches == 0
mblossvals = []
cliprangenow = cliprange(frac)
envsperbatch = nenvs // nminibatches
envinds = np.arange(nenvs)
flatinds = np.arange(nenvs * nsteps).reshape(nenvs, nsteps)
envsperbatch = nbatch_train // nsteps
states = states[:-1] # ignore the last state
for _ in range(noptepochs):
np.random.shuffle(envinds)
for start in range(0, nenvs, envsperbatch):
end = start + envsperbatch
mbenvinds = envinds[start:end]
mbflatinds = flatinds[mbenvinds].ravel()
slices = list(arr[mbflatinds]
for arr in (returns, masks, actions,
values, neglogpacs))
slices.insert(0, (obs[0][mbflatinds],
[obs[1][ix] for ix in mbflatinds]))
mblossvals.append(
model.train(lrnow, cliprangenow, *slices,
mbstates))
lossvals = np.mean(mblossvals, axis=0)
tnow = time.time()
fps = int(nbatch / (tnow - tstart))
if update % log_interval == 0 or update == 1:
ev = explained_variance(values, returns)
logger.logkv("serial_timesteps", update * nsteps)
logger.logkv("nupdates", update)
logger.logkv("total_timesteps", update * nbatch)
logger.logkv("fps", fps)
logger.logkv("explained_variance", float(ev))
logger.logkv('eprewmean',
safemean([epinfo['r'] for epinfo in epinfobuf]))
logger.logkv('eplenmean',
safemean([epinfo['l'] for epinfo in epinfobuf]))
logger.logkv('time_elapsed', tnow - tfirststart)
for (lossval, lossname) in zip(lossvals, model.loss_names):
logger.logkv(lossname, lossval)
logger.dumpkvs()
if save_interval and (update % save_interval == 0
or update == 1) and nmap_args['savepath']:
checkdir = nmap_args['savepath']
os.makedirs(checkdir, exist_ok=True)
savepath = osp.join(checkdir, '%.5i' % update)
print('Saving to', savepath)
model.save(path=savepath)
else:
model.load(load)
epinfobuf = deque(maxlen=100)
rewards_list = []
nupdates = total_timesteps // nbatch
for update in range(1, nupdates + 1):
obs_img = env.reset()
obs = [obs_img, env.initial_info()]
states = model.initial_state
dones = [False]
print('Test episode number = ', update)
assert nbatch % nminibatches == 0
nbatch_train = nbatch // nminibatches
tstart = time.time()
frac = 1.0 - (update - 1.0) / nupdates
lrnow = lr(frac)
cliprangenow = cliprange(frac)
mb_rewards = []
while not dones[-1]:
actions, values, mem, old_c_t, ctx_state, neglogpacs = model.step(
obs, states, dones)
states = (mem, np.expand_dims(old_c_t, 1), ctx_state)
obs, rewards, dones, infos = env.step(actions)
env.envs[0].render()
states = model.act_model.get_initial_state(1, states, dones)
obs = [obs, infos]
mb_rewards.append(rewards)
mb_rewards = np.asarray(mb_rewards, dtype=np.float32)
rewards_list.append(np.sum(mb_rewards))
for i in range(0, nupdates):
print('Test rewards for episode', i, 'is= ', rewards_list[i])
print('Average test rewards = ', np.mean(rewards_list))
env.close()
def learn(env, policy_fn, *,
timesteps_per_actorbatch, # timesteps per actor per update
clip_param, entcoeff, # clipping parameter epsilon, entropy coeff
optim_epochs, optim_stepsize, optim_batchsize,# optimization hypers
gamma, lam, # advantage estimation
max_timesteps=0, max_episodes=0, max_iters=0, max_seconds=0, # time constraint
callback=None, # you can do anything in the callback, since it takes locals(), globals()
adam_epsilon=1e-5,
schedule='constant' # annealing for stepsize parameters (epsilon and adam)
):
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_fn("pi", ob_space, ac_space) # Construct network for new policy
oldpi = policy_fn("oldpi", ob_space, ac_space) # Network for old policy
atarg = tf.placeholder(dtype=tf.float32, shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
lrmult = tf.placeholder(name='lrmult', dtype=tf.float32, shape=[]) # learning rate multiplier, updated with schedule
clip_param = clip_param * lrmult # Annealed cliping parameter epislon
ob = U.get_placeholder_cached(name="ob")
ac = pi.pdtype.sample_placeholder([None])
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
pol_entpen = (-entcoeff) * meanent
ratio = tf.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac)) # pnew / pold
surr1 = ratio * atarg # surrogate from conservative policy iteration
surr2 = tf.clip_by_value(ratio, 1.0 - clip_param, 1.0 + clip_param) * atarg #
pol_surr = - tf.reduce_mean(tf.minimum(surr1, surr2)) # PPO's pessimistic surrogate (L^CLIP)
vf_loss = tf.reduce_mean(tf.square(pi.vpred - ret))
total_loss = pol_surr + pol_entpen + vf_loss
losses = [pol_surr, pol_entpen, vf_loss, meankl, meanent]
loss_names = ["pol_surr", "pol_entpen", "vf_loss", "kl", "ent"]
var_list = pi.get_trainable_variables()
lossandgrad = U.function([ob, ac, atarg, ret, lrmult], losses + [U.flatgrad(total_loss, var_list)])
adam = MpiAdam(var_list, epsilon=adam_epsilon)
assign_old_eq_new = U.function([],[], updates=[tf.assign(oldv, newv)
for (oldv, newv) in zipsame(oldpi.get_variables(), pi.get_variables())])
compute_losses = U.function([ob, ac, atarg, ret, lrmult], losses)
U.initialize()
adam.sync()
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi, env, timesteps_per_actorbatch, stochastic=True)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=100) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=100) # rolling buffer for episode rewards
assert sum([max_iters>0, max_timesteps>0, max_episodes>0, max_seconds>0])==1, "Only one time constraint permitted"
while True:
if callback: callback(locals(), globals())
if max_timesteps and timesteps_so_far >= max_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
elif max_seconds and time.time() - tstart >= max_seconds:
break
if schedule == 'constant':
cur_lrmult = 1.0
elif schedule == 'linear':
cur_lrmult = max(1.0 - float(timesteps_so_far) / max_timesteps, 0)
else:
raise NotImplementedError
logger.log("********** Iteration %i ************"%iters_so_far)
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, gamma, lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg["tdlamret"]
vpredbefore = seg["vpred"] # predicted value function before udpate
atarg = (atarg - atarg.mean()) / atarg.std() # standardized advantage function estimate
d = Dataset(dict(ob=ob, ac=ac, atarg=atarg, vtarg=tdlamret), shuffle=not pi.recurrent)
optim_batchsize = optim_batchsize or ob.shape[0]
if hasattr(pi, "ob_rms"): pi.ob_rms.update(ob) # update running mean/std for policy
assign_old_eq_new() # set old parameter values to new parameter values
logger.log("Optimizing...")
logger.log(fmt_row(13, loss_names))
# Here we do a bunch of optimization epochs over the data
for _ in range(optim_epochs):
losses = [] # list of tuples, each of which gives the loss for a minibatch
for batch in d.iterate_once(optim_batchsize):
*newlosses, g = lossandgrad(batch["ob"], batch["ac"], batch["atarg"], batch["vtarg"], cur_lrmult)
adam.update(g, optim_stepsize * cur_lrmult)
losses.append(newlosses)
logger.log(fmt_row(13, np.mean(losses, axis=0)))
logger.log("Evaluating losses...")
losses = []
for batch in d.iterate_once(optim_batchsize):
newlosses = compute_losses(batch["ob"], batch["ac"], batch["atarg"], batch["vtarg"], cur_lrmult)
losses.append(newlosses)
meanlosses,_,_ = mpi_moments(losses, axis=0)
logger.log(fmt_row(13, meanlosses))
for (lossval, name) in zipsame(meanlosses, loss_names):
logger.record_tabular("loss_"+name, lossval)
logger.record_tabular("ev_tdlam_before", explained_variance(vpredbefore, tdlamret))
lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
if MPI.COMM_WORLD.Get_rank()==0:
logger.dump_tabular()
def learn(policy,
env,
seed,
total_timesteps=int(40e6),
gamma=0.99,
log_interval=10,
nprocs=32,
nsteps=20,
nstack=4,
ent_coef=0.01,
vf_coef=0.5,
vf_fisher_coef=1.0,
lr=0.25,
max_grad_norm=0.5,
kfac_clip=0.001,
save_interval=None,
lrschedule='linear'):
tf.reset_default_graph()
set_global_seeds(seed)
nenvs = env.num_envs
ob_space = env.observation_space
ac_space = env.action_space
make_model = lambda: Model(policy,
ob_space,
ac_space,
nenvs,
total_timesteps,
nprocs=nprocs,
nsteps=nsteps,
nstack=nstack,
ent_coef=ent_coef,
vf_coef=vf_coef,
vf_fisher_coef=vf_fisher_coef,
lr=lr,
max_grad_norm=max_grad_norm,
kfac_clip=kfac_clip,
lrschedule=lrschedule)
if save_interval and logger.get_dir():
with open(osp.join(logger.get_dir(), 'make_model.pkl'), 'wb') as fh:
fh.write(cloudpickle.dumps(make_model))
model = make_model()
# try to load the model from a previous save
# This requires the operator to copy a model to the parent
# directory of the logging dir (typically /tmp) as "checkpoint_model"
if logger.get_dir():
logger_parent_dir = osp.abspath(osp.join(logger.get_dir(), os.pardir))
maybe_load_model(logger_parent_dir, model)
runner = Runner(env, model, nsteps=nsteps, nstack=nstack, gamma=gamma)
nbatch = nenvs * nsteps
tstart = time.time()
coord = tf.train.Coordinator()
enqueue_threads = model.q_runner.create_threads(model.sess,
coord=coord,
start=True)
for update in range(1, total_timesteps // nbatch + 1):
obs, states, rewards, masks, actions, values = runner.run()
policy_loss, value_loss, policy_entropy = model.train(
obs, states, rewards, masks, actions, values)
model.old_obs = obs
nseconds = time.time() - tstart
fps = int((update * nbatch) / nseconds)
if update % log_interval == 0 or update == 1:
ev = explained_variance(values, rewards)
logger.record_tabular("nupdates", update)
logger.record_tabular("total_timesteps", update * nbatch)
logger.record_tabular("fps", fps)
logger.record_tabular("policy_entropy", float(policy_entropy))
logger.record_tabular("policy_loss", float(policy_loss))
logger.record_tabular("value_loss", float(value_loss))
logger.record_tabular("explained_variance", float(ev))
logger.dump_tabular()
if save_interval and (update % save_interval == 0
or update == 1) and logger.get_dir():
savepath = osp.join(logger.get_dir(), 'checkpoint%.5i' % update)
print('Saving to', savepath)
model.save(savepath)
# always save the model when we stop training, if we have a place to save to
if logger.get_dir():
savepath = osp.join(logger.get_dir(), 'final_model')
print('Saving to', savepath)
model.save(savepath)
coord.request_stop()
coord.join(enqueue_threads)
env.close()
def learn(policy,
env,
nsteps,
total_timesteps,
gamma,
lam,
vf_coef,
ent_coef,
lr,
cliprange,
max_grad_norm,
log_interval):
noptepochs = 4
nminibatches = 8
if isinstance(lr, float): lr = constfn(lr)
else: assert callable(lr)
if isinstance(cliprange, float): cliprange = constfn(cliprange)
else: assert callable(cliprange)
# Get the nb of env
nenvs = env.num_envs
# Get state_space and action_space
ob_space = env.observation_space
ac_space = env.action_space
# Calculate the batch_size
batch_size = nenvs * nsteps # For instance if we take 5 steps and we have 5 environments batch_size = 25
batch_train_size = batch_size // nminibatches
assert batch_size % nminibatches == 0
# Instantiate the model object (that creates step_model and train_model)
model = Model(policy=policy,
ob_space=ob_space,
action_space=ac_space,
nenvs=nenvs,
nsteps=nsteps,
ent_coef=ent_coef,
vf_coef=vf_coef,
max_grad_norm=max_grad_norm)
# Load the model
# If you want to continue training
# load_path = "./models/40/model.ckpt"
# model.load(load_path)
# Instantiate the runner object
runner = Runner(env, model, nsteps=nsteps, total_timesteps=total_timesteps, gamma=gamma, lam=lam)
# Start total timer
tfirststart = time.time()
nupdates = total_timesteps//batch_size+1
for update in range(1, nupdates+1):
# Start timer
tstart = time.time()
frac = 1.0 - (update - 1.0) / nupdates
# Calculate the learning rate
lrnow = lr(frac)
# Calculate the cliprange
cliprangenow = cliprange(frac)
# Get minibatch
obs, actions, returns, values, neglogpacs = runner.run()
# Here what we're going to do is for each minibatch calculate the loss and append it.
mb_losses = []
total_batches_train = 0
# Index of each element of batch_size
# Create the indices array
indices = np.arange(batch_size)
for _ in range(noptepochs):
# Randomize the indexes
np.random.shuffle(indices)
# 0 to batch_size with batch_train_size step
for start in range(0, batch_size, batch_train_size):
end = start + batch_train_size
mbinds = indices[start:end]
slices = (arr[mbinds] for arr in (obs, actions, returns, values, neglogpacs))
mb_losses.append(model.train(*slices, lrnow, cliprangenow))
# Feedforward --> get losses --> update
lossvalues = np.mean(mb_losses, axis=0)
# End timer
tnow = time.time()
# Calculate the fps (frame per second)
fps = int(batch_size / (tnow - tstart))
if update % log_interval == 0 or update == 1:
"""
Computes fraction of variance that ypred explains about y.
Returns 1 - Var[y-ypred] / Var[y]
interpretation:
ev=0 => might as well have predicted zero
ev=1 => perfect prediction
ev<0 => worse than just predicting zero
"""
ev = explained_variance(values, returns)
logger.record_tabular("serial_timesteps", update*nsteps)
logger.record_tabular("nupdates", update)
logger.record_tabular("total_timesteps", update*batch_size)
logger.record_tabular("fps", fps)
logger.record_tabular("policy_loss", float(lossvalues[0]))
logger.record_tabular("policy_entropy", float(lossvalues[2]))
logger.record_tabular("value_loss", float(lossvalues[1]))
logger.record_tabular("explained_variance", float(ev))
logger.record_tabular("time elapsed", float(tnow - tfirststart))
savepath = "./models/" + str(update) + "/model.ckpt"
model.save(savepath)
print('Saving to', savepath)
# Test our agent with 3 trials and mean the score
# This will be useful to see if our agent is improving
test_score = testing(model)
logger.record_tabular("Mean score test level", test_score)
logger.dump_tabular()
env.close()
def learn(*, network, env, total_timesteps, eval_env = None,
seed=None, nsteps=2048, ent_coef=0.0, lr=3e-4,
vf_coef=0.5, max_grad_norm=0.5, gamma=0.99, lam=0.95,
log_interval=10, nminibatches=4, noptepochs=4, cliprange=0.2,
save_interval=0, load_path=None, model_fn=None, **network_kwargs):
'''Learn policy using PPO algorithm (https://arxiv.org/abs/1707.06347)
Parameters:
----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See common/models.py/lstm for more details on using recurrent nets in policies
env: baselines.common.vec_env.VecEnv environment. Needs to be vectorized for parallel environment simulation.
The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class.
nsteps: int number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel)
Daniel: should be `T` in the paper. Atari defaults are 128 as in the paper.
total_timesteps: int number of timesteps (i.e. number of actions taken in the environment)
ent_coef: float policy entropy coefficient in the optimization objective
lr: float or function learning rate, constant or a schedule function [0,1] -> R+ where 1 is beginning of the
training and 0 is the end of the training.
vf_coef: float value function loss coefficient in the optimization objective
Daniel: 0.5 by default but the PPO paper uses 1.0 for Atari games.
max_grad_norm: float or None gradient norm clipping coefficient
gamma: float discounting factor
lam: float advantage estimation discounting factor (lambda in the paper)
log_interval: int number of timesteps between logging events
nminibatches: int number of training minibatches per update. For recurrent policies,
should be smaller or equal than number of environments run in parallel.
noptepochs: int number of training epochs per update
Daniel: 4 by default but the PPO paper uses 3 (for Atari games), etc.
cliprange: float or function clipping range, constant or schedule function [0,1] -> R+ where 1 is beginning of the training
and 0 is the end of the training. Daniel: for `tf.clip_by_value(ratio, 1-cliprange, 1+cliprange)`
save_interval: int number of timesteps between saving events
load_path: str path to load the model from
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
'''
set_global_seeds(seed)
if isinstance(lr, float): lr = constfn(lr)
else: assert callable(lr)
if isinstance(cliprange, float): cliprange = constfn(cliprange)
else: assert callable(cliprange)
total_timesteps = int(total_timesteps)
# Daniel: hacky within PPO2 solution for limiting action ranges.
if 'limit_act_range' in network_kwargs:
limit_act_range = network_kwargs['limit_act_range']
network_kwargs.pop('limit_act_range')
else:
limit_act_range = False
policy = build_policy(env, network, limit_act_range=limit_act_range, **network_kwargs)
# Get the nb of env
nenvs = env.num_envs
# Get state_space and action_space
ob_space = env.observation_space
ac_space = env.action_space
# Calculate the batch_size
nbatch = nenvs * nsteps
nbatch_train = nbatch // nminibatches
# Instantiate the model object (that creates act_model and train_model)
if model_fn is None:
from baselines.ppo2.model import Model
model_fn = Model
model = model_fn(policy=policy, ob_space=ob_space, ac_space=ac_space,
nbatch_act=nenvs, nbatch_train=nbatch_train,
nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef,
max_grad_norm=max_grad_norm)
if load_path is not None:
logger.info("\nInside ppo2, loading model from: {}".format(load_path))
model.load(load_path)
# Daniel: debugging and sanity checks
_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
tf_util.display_var_info(_variables)
# Instantiate the runner object (Daniel: calls `env.reset()` so can take a while for cloth)
# Also, I'm going to assume that if total_timesteps=0 then we don't waste time creating this.
if total_timesteps > 0:
runner = Runner(env=env, model=model, nsteps=nsteps, gamma=gamma, lam=lam)
if eval_env is not None:
eval_runner = Runner(env = eval_env, model = model, nsteps = nsteps, gamma = gamma, lam= lam)
epinfobuf = deque(maxlen=100)
if eval_env is not None:
eval_epinfobuf = deque(maxlen=100)
# Start total timer
tfirststart = time.perf_counter()
nupdates = total_timesteps//nbatch
# Daniel: debugging and sanity checks
logger.info("\nInside ppo2, before updates (`env.reset()` called before this)")
logger.info(" nsteps: {}, each env in VecEnv does this many to get minibatch".format(nsteps))
logger.info(" nbatch: {}, i.e., nsteps * nenv, size of data from (get_minibatch)".format(nbatch))
logger.info(" nbatch_train: {}, batch size for actual gradient update within epoch".format(nbatch_train))
logger.info(" noptepochs: {}, number of epochs over collected minibatch for PPO updates".format(noptepochs))
logger.info(" nupdates: {}, number of (get_minibatch, update_net) cycles".format(nupdates))
logger.info(" our model_fn class: {}".format(model_fn))
logger.info("(end of debugging messages)\n")
for update in range(1, nupdates+1):
assert nbatch % nminibatches == 0
# Start timer
tstart = time.perf_counter()
frac = 1.0 - (update - 1.0) / nupdates
# Calculate the learning rate
lrnow = lr(frac)
# Calculate the cliprange
cliprangenow = cliprange(frac)
# Get minibatch
obs, returns, masks, actions, values, neglogpacs, states, epinfos, ep_all_infos = runner.run() #pylint: disable=E0632
if eval_env is not None:
eval_obs, eval_returns, eval_masks, eval_actions, eval_values, eval_neglogpacs, eval_states, eval_epinfos, eval_ep_all_infos = eval_runner.run() #pylint: disable=E0632
epinfobuf.extend(epinfos)
if eval_env is not None:
eval_epinfobuf.extend(eval_epinfos)
# Here what we're going to do is for each minibatch calculate the loss and append it.
mblossvals = []
if states is None: # nonrecurrent version
# Index of each element of batch_size
# Create the indices array
inds = np.arange(nbatch)
for _ in range(noptepochs):
# Randomize the indexes
np.random.shuffle(inds)
# 0 to batch_size with batch_train_size step
for start in range(0, nbatch, nbatch_train):
end = start + nbatch_train
mbinds = inds[start:end]
slices = (arr[mbinds] for arr in (obs, returns, masks, actions, values, neglogpacs))
mblossvals.append(model.train(lrnow, cliprangenow, *slices))
else: # recurrent version
assert nenvs % nminibatches == 0
envsperbatch = nenvs // nminibatches
envinds = np.arange(nenvs)
flatinds = np.arange(nenvs * nsteps).reshape(nenvs, nsteps)
for _ in range(noptepochs):
np.random.shuffle(envinds)
for start in range(0, nenvs, envsperbatch):
end = start + envsperbatch
mbenvinds = envinds[start:end]
mbflatinds = flatinds[mbenvinds].ravel()
slices = (arr[mbflatinds] for arr in (obs, returns, masks, actions, values, neglogpacs))
mbstates = states[mbenvinds]
mblossvals.append(model.train(lrnow, cliprangenow, *slices, mbstates))
# Feedforward --> get losses --> update
lossvals = np.mean(mblossvals, axis=0)
# End timer
tnow = time.perf_counter()
# Calculate the fps (frame per second)
fps = int(nbatch / (tnow - tstart))
if update % log_interval == 0 or update == 1:
# Calculates if value function is a good predicator of the returns (ev > 1)
# or if it's just worse than predicting nothing (ev =< 0)
ev = explained_variance(values, returns)
logger.logkv("serial_timesteps", update*nsteps)
logger.logkv("nupdates", update)
logger.logkv("total_timesteps", update*nbatch)
logger.logkv("fps", fps)
logger.logkv("explained_variance", float(ev))
logger.logkv('eprewmean', safemean([epinfo['r'] for epinfo in epinfobuf]))
logger.logkv('eplenmean', safemean([epinfo['l'] for epinfo in epinfobuf]))
if eval_env is not None:
logger.logkv('eval_eprewmean', safemean([epinfo['r'] for epinfo in eval_epinfobuf]) )
logger.logkv('eval_eplenmean', safemean([epinfo['l'] for epinfo in eval_epinfobuf]) )
logger.logkv('time_elapsed', tnow - tfirststart)
for (lossval, lossname) in zip(lossvals, model.loss_names):
logger.logkv(lossname, lossval)
if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
logger.dumpkvs()
if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir() and (MPI is None or MPI.COMM_WORLD.Get_rank() == 0):
checkdir = osp.join(logger.get_dir(), 'checkpoints')
os.makedirs(checkdir, exist_ok=True)
savepath = osp.join(checkdir, '%.5i'%update)
logger.info('Saving model checkpoint to: ', savepath)
model.save(savepath)
# ------------------------------------------------------------------
# Daniel: extra stuff for debugging PPO on cloth, actions and infos for each episode.
logstd_vals = model.act_model.get_logstd_values()
action_dir = osp.join(logger.get_dir(), 'actions')
episode_dir = osp.join(logger.get_dir(), 'ep_all_infos')
logstd_dir = osp.join(logger.get_dir(), 'logstd')
os.makedirs(action_dir, exist_ok=True)
os.makedirs(episode_dir, exist_ok=True)
os.makedirs(logstd_dir, exist_ok=True)
act_savepath = osp.join(action_dir, 'actions_%.5i.pkl'%update)
epi_savepath = osp.join(episode_dir, 'infos_%.5i.pkl'%update)
std_savepath = osp.join(logstd_dir, 'logstd_%.5i.pkl'%update)
with open(act_savepath, 'wb') as fh:
pickle.dump(actions, fh)
with open(epi_savepath, 'wb') as fh:
pickle.dump(ep_all_infos, fh)
with open(std_savepath, 'wb') as fh:
pickle.dump(logstd_vals, fh)
# ------------------------------------------------------------------
return model
def learn(
network,
env,
seed=None,
nsteps=5,
total_timesteps=int(80e6),
vf_coef=0.5,
ent_coef=0.01,
max_grad_norm=0.5,
lr=7e-4,
lrschedule='linear',
epsilon=1e-5,
alpha=0.99,
gamma=0.99,
log_interval=100,
load_path=None,
**network_kwargs):
'''
Main entrypoint for A2C algorithm. Train a policy with given network architecture on a given environment using a2c algorithm.
Parameters:
-----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See baselines.common/policies.py/lstm for more details on using recurrent nets in policies
env: RL environment. Should implement interface similar to VecEnv (baselines.common/vec_env) or be wrapped with DummyVecEnv (baselines.common/vec_env/dummy_vec_env.py)
seed: seed to make random number sequence in the alorightm reproducible. By default is None which means seed from system noise generator (not reproducible)
nsteps: int, number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel)
total_timesteps: int, total number of timesteps to train on (default: 80M)
vf_coef: float, coefficient in front of value function loss in the total loss function (default: 0.5)
ent_coef: float, coeffictiant in front of the policy entropy in the total loss function (default: 0.01)
max_gradient_norm: float, gradient is clipped to have global L2 norm no more than this value (default: 0.5)
lr: float, learning rate for RMSProp (current implementation has RMSProp hardcoded in) (default: 7e-4)
lrschedule: schedule of learning rate. Can be 'linear', 'constant', or a function [0..1] -> [0..1] that takes fraction of the training progress as input and
returns fraction of the learning rate (specified as lr) as output
epsilon: float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5)
alpha: float, RMSProp decay parameter (default: 0.99)
gamma: float, reward discounting parameter (default: 0.99)
log_interval: int, specifies how frequently the logs are printed out (default: 100)
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
'''
set_global_seeds(seed)
# Get the nb of env
nenvs = env.num_envs
policy = build_policy(env, network, **network_kwargs)
# Instantiate the model object (that creates step_model and train_model)
model = Model(policy=policy, env=env, nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef,
max_grad_norm=max_grad_norm, lr=lr, alpha=alpha, epsilon=epsilon, total_timesteps=total_timesteps, lrschedule=lrschedule)
if load_path is not None:
model.load(load_path)
# Instantiate the runner object
runner = Runner(env, model, nsteps=nsteps, gamma=gamma)
epinfobuf = deque(maxlen=100)
# Calculate the batch_size
nbatch = nenvs*nsteps
# Start total timer
tstart = time.time()
for update in range(1, total_timesteps//nbatch+1):
# Get mini batch of experiences
obs, states, rewards, masks, actions, values, epinfos = runner.run()
epinfobuf.extend(epinfos)
policy_loss, value_loss, policy_entropy = model.train(obs, states, rewards, masks, actions, values)
nseconds = time.time()-tstart
# Calculate the fps (frame per second)
fps = int((update*nbatch)/nseconds)
if update % log_interval == 0 or update == 1:
# Calculates if value function is a good predicator of the returns (ev > 1)
# or if it's just worse than predicting nothing (ev =< 0)
ev = explained_variance(values, rewards)
logger.record_tabular("nupdates", update)
logger.record_tabular("total_timesteps", update*nbatch)
logger.record_tabular("fps", fps)
logger.record_tabular("policy_entropy", float(policy_entropy))
logger.record_tabular("value_loss", float(value_loss))
logger.record_tabular("explained_variance", float(ev))
logger.record_tabular("eprewmean", safemean([epinfo['r'] for epinfo in epinfobuf]))
logger.record_tabular("eplenmean", safemean([epinfo['l'] for epinfo in epinfobuf]))
logger.dump_tabular()
return model
def fit(self, paths, targvals):
X = np.concatenate([self._preproc(p) for p in paths])
y = np.concatenate(targvals)
logger.record_tabular("EVBefore", common.explained_variance(self._predict(X), y))
for _ in range(25): self.do_update(X, y)
logger.record_tabular("EVAfter", common.explained_variance(self._predict(X), y))