def evaluate(env,
policy_func,
load_model_path,
stochastic_policy=False,
number_trajs=10):
from gail.trpo_mpi import traj_episode_generator
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_func("pi", ob_space,
ac_space) # Construct network for new policy
# placeholder
ob = U.get_placeholder_cached(name="ob")
ac = pi.pdtype.sample_placeholder([None])
stochastic = U.get_placeholder_cached(name="stochastic")
ep_gen = traj_episode_generator(pi,
env,
1024,
stochastic=stochastic_policy)
U.load_state(load_model_path)
len_list = []
ret_list = []
for _ in tqdm(range(number_trajs)):
traj = ep_gen.__next__()
ep_len, ep_ret = traj['ep_len'], traj['ep_ret']
len_list.append(ep_len)
ret_list.append(ep_ret)
if stochastic_policy:
print('stochastic policy:')
else:
print('deterministic policy:')
print("Average length:", sum(len_list) / len(len_list))
print("Average return:", sum(ret_list) / len(ret_list))
def learn(env,
policy_func,
dataset,
pretrained,
optim_batch_size=128,
max_iters=1e4,
adam_epsilon=1e-5,
optim_stepsize=3e-4,
ckpt_dir=None,
log_dir=None,
task_name=None):
val_per_iter = int(max_iters / 10)
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_func("pi", ob_space,
ac_space) # Construct network for new policy
# placeholder
ob = U.get_placeholder_cached(name="ob")
ac = pi.pdtype.sample_placeholder([None])
stochastic = U.get_placeholder_cached(name="stochastic")
loss = tf.reduce_mean(tf.square(ac - pi.ac))
var_list = pi.get_trainable_variables()
adam = MpiAdam(var_list, epsilon=adam_epsilon)
lossandgrad = U.function([ob, ac, stochastic],
[loss] + [U.flatgrad(loss, var_list)])
if not pretrained:
writer = U.FileWriter(log_dir)
ep_stats = stats(["Loss"])
U.initialize()
adam.sync()
logger.log("Pretraining with Behavior Cloning...")
for iter_so_far in tqdm(range(int(max_iters))):
ob_expert, ac_expert = dataset.get_next_batch(optim_batch_size,
'train')
loss, g = lossandgrad(ob_expert, ac_expert, True)
adam.update(g, optim_stepsize)
if not pretrained:
ep_stats.add_all_summary(writer, [loss], iter_so_far)
if iter_so_far % val_per_iter == 0:
ob_expert, ac_expert = dataset.get_next_batch(-1, 'val')
loss, g = lossandgrad(ob_expert, ac_expert, False)
logger.log("Validation:")
logger.log("Loss: %f" % loss)
if not pretrained:
U.save_state(os.path.join(ckpt_dir, task_name),
counter=iter_so_far)
if pretrained:
savedir_fname = tempfile.TemporaryDirectory().name
U.save_state(savedir_fname, var_list=pi.get_variables())
return savedir_fname
def learn(
env,
model_path,
data_path,
policy_fn,
*,
horizon=150, # timesteps per actor per update
rolloutSize=50,
clip_param=0.2,
entcoeff=0.02, # clipping parameter epsilon, entropy coeff
optim_epochs=10,
optim_stepsize=3e-4,
optim_batchsize=32, # optimization hypers
gamma=0.99,
lam=0.95, # advantage estimation
max_iters=0, # time constraint
adam_epsilon=1e-4,
schedule='constant', # annealing for stepsize parameters (epsilon and adam)
retrain=False):
# Setup losses and policy
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
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
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=5) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=5) # rolling buffer for episode rewards
p = [] # for saving the rollouts
if retrain == True:
print("Retraining the policy from saved path")
time.sleep(2)
U.load_state(model_path)
max_timesteps = int(horizon * rolloutSize * max_iters)
while True:
if max_iters and iters_so_far >= max_iters:
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)
print("Collecting samples for policy optimization !! ")
if iters_so_far > 70:
render = True
else:
render = False
rollouts = sample_trajectory(pi,
env,
horizon=horizon,
rolloutSize=rolloutSize,
stochastic=True,
render=render)
# Save rollouts
data = {'rollouts': rollouts}
p.append(data)
del data
data_file_name = data_path + 'rollout_data.pkl'
pickle.dump(p, open(data_file_name, "wb"))
add_vtarg_and_adv(rollouts, gamma, lam)
ob, ac, atarg, tdlamret = rollouts["ob"], rollouts["ac"], rollouts[
"adv"], rollouts["tdlamret"]
atarg = (atarg - atarg.mean()
) / atarg.std() # standardized advantage function estimate
d = Dataset(dict(ob=ob, ac=ac, atarg=atarg, vtarg=tdlamret),
deterministic=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...")
# 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)
lrlocal = (rollouts["ep_lens"], rollouts["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("Success", rollouts["success"])
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(
env,
policy_func,
discriminator,
expert_dataset,
embedding_z,
pretrained,
pretrained_weight,
*,
g_step,
d_step,
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,
d_stepsize=3e-4,
vf_iters=3,
max_timesteps=0,
max_episodes=0,
max_iters=0, # time constraint
callback=None,
save_per_iter=100,
ckpt_dir=None,
log_dir=None,
load_model_path=None,
task_name=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 = 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")
]
d_adam = MpiAdam(discriminator.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(
[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
writer = U.FileWriter(log_dir)
U.initialize()
th_init = get_flat()
MPI.COMM_WORLD.Bcast(th_init, root=0)
set_from_flat(th_init)
d_adam.sync()
vfadam.sync()
print("Init param sum", th_init.sum(), flush=True)
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi,
env,
discriminator,
embedding=embedding_z,
timesteps_per_batch=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(discriminator.loss_name)
ep_stats = stats(["True_rewards", "Rewards", "Episode_length"])
# if provide pretrained weight
if pretrained_weight is not None:
U.load_state(pretrained_weight, var_list=pi.get_variables())
# if provieded model path
if load_model_path is not None:
U.load_state(load_model_path)
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 iters_so_far % save_per_iter == 0 and ckpt_dir is not None:
U.save_state(os.path.join(ckpt_dir, task_name),
counter=iters_so_far)
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, discriminator.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 discriminator
if hasattr(discriminator, "obs_rms"):
discriminator.obs_rms.update(
np.concatenate((ob_batch, ob_expert), 0))
*newlosses, g = discriminator.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 rank == 0:
logger.dump_tabular()
g_loss_stats.add_all_summary(writer, g_losses, iters_so_far)
d_loss_stats.add_all_summary(writer, np.mean(d_losses, axis=0),
iters_so_far)
ep_stats.add_all_summary(writer, [
np.mean(true_rewbuffer),
np.mean(rewbuffer),
np.mean(lenbuffer)
], iters_so_far)
def learn(env, model_path, data_path, policy_fn, *,
rolloutSize, num_options=4, horizon=80,
clip_param=0.025, ent_coeff=0.01, # clipping parameter epsilon, entropy coeff
optim_epochs=10, mainlr=3.25e-4, intlr=1e-4, piolr=1e-4, termlr=5e-7, optim_batchsize=100, # optimization hypers
gamma=0.99, lam=0.95, # advantage estimation
max_iters=20, # time constraint
adam_epsilon=1e-5,
schedule='constant', # annealing for stepsize parameters (epsilon and adam)
retrain=False,
):
"""
Core learning function
"""
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_fn("pi", ob_space, ac_space, num_options=num_options) # Construct network for new policy
oldpi = policy_fn("oldpi", ob_space, ac_space, num_options=num_options) # 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")
option = U.get_placeholder_cached(name="option")
term_adv = U.get_placeholder(name='term_adv', dtype=tf.float32, shape=[None])
op_adv = tf.placeholder(dtype=tf.float32, shape=[None]) # Target advantage function (if applicable)
betas = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
ac = pi.pdtype.sample_placeholder([None])
# Setup losses and stuff
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
pol_entpen = (-ent_coeff) * 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"]
term_loss = pi.tpred * term_adv
activated_options = tf.placeholder(dtype=tf.float32, shape=[None, num_options])
pi_w = tf.placeholder(dtype=tf.float32, shape=[None, num_options])
option_hot = tf.one_hot(option, depth=num_options)
pi_I = (pi.intfc * activated_options) * pi_w / tf.expand_dims(
tf.reduce_sum((pi.intfc * activated_options) * pi_w, axis=1), 1)
pi_I = tf.clip_by_value(pi_I, 1e-6, 1 - 1e-6)
int_loss = - tf.reduce_sum(betas * tf.reduce_sum(pi_I * option_hot, axis=1) * op_adv)
intfc = tf.placeholder(dtype=tf.float32, shape=[None, num_options])
pi_I = (intfc * activated_options) * pi.op_pi / tf.expand_dims(
tf.reduce_sum((intfc * activated_options) * pi.op_pi, axis=1), 1)
pi_I = tf.clip_by_value(pi_I, 1e-6, 1 - 1e-6)
op_loss = - tf.reduce_sum(betas * tf.reduce_sum(pi_I * option_hot, axis=1) * op_adv)
log_pi = tf.log(tf.clip_by_value(pi.op_pi, 1e-20, 1.0))
op_entropy = -tf.reduce_mean(pi.op_pi * log_pi, reduction_indices=1)
op_loss -= 0.01 * tf.reduce_sum(op_entropy)
var_list = pi.get_trainable_variables()
lossandgrad = U.function([ob, ac, atarg, ret, lrmult, option], losses + [U.flatgrad(total_loss, var_list)])
termgrad = U.function([ob, option, term_adv],
[U.flatgrad(term_loss, var_list)]) # Since we will use a different step size.
opgrad = U.function([ob, option, betas, op_adv, intfc, activated_options],
[U.flatgrad(op_loss, var_list)]) # Since we will use a different step size.
intgrad = U.function([ob, option, betas, op_adv, pi_w, activated_options],
[U.flatgrad(int_loss, var_list)]) # Since we will use a different step size.
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, option], losses)
U.initialize()
adam.sync()
episodes_so_far = 0
timesteps_so_far = 0
global iters_so_far
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=5) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=5) # rolling buffer for episode rewards
datas = [0 for _ in range(num_options)]
if retrain:
print("Retraining to New Task !! ")
time.sleep(2)
U.load_state(model_path+'/')
p = []
max_timesteps = int(horizon * rolloutSize * max_iters)
while True:
if max_iters and iters_so_far >= max_iters:
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)
render = False
rollouts = sample_trajectory(pi, env, horizon=horizon, rolloutSize=rolloutSize, render=render)
# Save rollouts
data = {'rollouts': rollouts}
p.append(data)
del data
data_file_name = data_path + 'rollout_data.pkl'
pickle.dump(p, open(data_file_name, "wb"))
add_vtarg_and_adv(rollouts, gamma, lam, num_options)
opt_d = []
for i in range(num_options):
dur = np.mean(rollouts['opt_dur'][i]) if len(rollouts['opt_dur'][i]) > 0 else 0.
opt_d.append(dur)
ob, ac, opts, atarg, tdlamret = rollouts["ob"], rollouts["ac"], rollouts["opts"], rollouts["adv"], rollouts["tdlamret"]
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
assign_old_eq_new() # set old parameter values to new parameter values
# Optimizing the policy
for opt in range(num_options):
indices = np.where(opts == opt)[0]
print("Option- ", opt, " Batch Size: ", indices.size)
opt_d[opt] = indices.size
if not indices.size:
continue
datas[opt] = d = Dataset(dict(ob=ob[indices], ac=ac[indices], atarg=atarg[indices], vtarg=tdlamret[indices]), shuffle=not pi.recurrent)
if indices.size < optim_batchsize:
print("Too few samples for opt - ", opt)
continue
optim_batchsize_corrected = optim_batchsize
optim_epochs_corrected = np.clip(np.int(indices.size / optim_batchsize_corrected), 1, optim_epochs)
print("Optim Epochs:", optim_epochs_corrected)
logger.log("Optimizing...")
# Here we do a bunch of optimization epochs over the data
for _ in range(optim_epochs_corrected):
losses = [] # list of tuples, each of which gives the loss for a minibatch
for batch in d.iterate_once(optim_batchsize_corrected):
*newlosses, grads = lossandgrad(batch["ob"], batch["ac"], batch["atarg"], batch["vtarg"],
cur_lrmult, [opt])
adam.update(grads, mainlr * cur_lrmult)
losses.append(newlosses)
# Optimize termination functions
termg = termgrad(rollouts["ob"], rollouts['opts'], rollouts["op_adv"])[0]
adam.update(termg, termlr)
# Optimize interest functions
intgrads = intgrad(rollouts['ob'], rollouts['opts'], rollouts["last_betas"], rollouts["op_adv"], rollouts["op_probs"], rollouts["activated_options"])[0]
adam.update(intgrads, intlr)
# Optimize policy over options
opgrads = opgrad(rollouts['ob'], rollouts['opts'], rollouts["last_betas"], rollouts["op_adv"], rollouts["intfc"], rollouts["activated_options"])[0]
adam.update(opgrads, piolr)
lrlocal = (rollouts["ep_lens"], rollouts["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("Success", rollouts["success"])
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(env, model_path, data_path, policy_fn, model_learning_params, svm_grid_params, svm_params_interest,
svm_params_guard, *, modes, rolloutSize, num_options=2,
horizon, # timesteps per actor per update
clip_param, ent_coeff=0.02, # clipping parameter epsilon, entropy coeff
optim_epochs=10, optim_stepsize=3e-4, optim_batchsize=160, # optimization hypers
gamma=0.99, lam=0.95, # advantage estimation
max_iters=0, # time constraint
adam_epsilon=1.2e-4,
schedule='linear', # annealing for stepsize parameters (epsilon and adam)
retrain=False
):
"""
Core learning function
"""
ob_space = env.observation_space
ac_space = env.action_space
if retrain:
model = pickle.load(open(model_path + '/hybrid_model.pkl', 'rb'))
print("Model graph:", model.transitionGraph.nodes)
print("Model options:", model.transitionGraph.edges)
else:
model = partialHybridModel(env, model_learning_params, svm_grid_params, svm_params_interest, svm_params_guard, horizon, modes, num_options, rolloutSize)
pi = policy_fn("pi", ob_space, ac_space, model, num_options) # Construct network for new policy
oldpi = policy_fn("oldpi", ob_space, ac_space, model, num_options) # Network for old policy
atarg = tf1.placeholder(dtype=tf1.float32, shape=[None]) # Target advantage function (if applicable)
ret = tf1.placeholder(dtype=tf1.float32, shape=[None]) # Empirical return
lrmult = tf1.placeholder(name='lrmult', dtype=tf1.float32,
shape=[]) # learning rate multiplier, updated with schedule
clip_param = clip_param * lrmult # Annealed cliping parameter epislon
# Define placeholders for computing the advantage
ob = U.get_placeholder_cached(name="ob")
option = U.get_placeholder_cached(name="option")
ac = pi.pdtype.sample_placeholder([None])
# Defining losses for optimization
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = tf1.reduce_mean(kloldnew)
meanent = tf1.reduce_mean(ent)
pol_entpen = (-ent_coeff) * meanent
ratio = tf1.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac)) # pnew / pold
surr1 = ratio * atarg # surrogate from conservative policy iteration
surr2 = tf1.clip_by_value(ratio, 1.0 - clip_param, 1.0 + clip_param) * atarg #
pol_surr = - tf1.reduce_mean(tf1.minimum(surr1, surr2)) # PPO's pessimistic surrogate (L^CLIP), negative to convert from a maximization to minimization problem
vf_loss = tf1.reduce_mean(tf1.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, option], losses + [U.flatgrad(total_loss, var_list)])
adam = MpiAdam(var_list, epsilon=adam_epsilon)
assign_old_eq_new = U.function([], [], updates=[tf1.assign(oldv, newv) for (oldv, newv) in
zipsame(oldpi.get_variables(), pi.get_variables())])
compute_losses = U.function([ob, ac, atarg, ret, lrmult, option], losses)
U.initialize()
adam.sync()
# Prepare for rollouts
episodes_so_far = 0
timesteps_so_far = 0
global iters_so_far
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=10) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=10) # rolling buffer for episode rewards
p = [] # for saving the rollouts
if retrain:
print("Retraining to New Task !!")
time.sleep(2)
U.load_state(model_path+'/')
print(pi.eps)
max_timesteps = int(horizon * rolloutSize * max_iters)
while True:
if max_iters and iters_so_far >= max_iters:
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)
print("Collecting samples for policy optimization !! ")
render = False
rollouts = sample_trajectory(pi, model, env, horizon=horizon, rolloutSize=rolloutSize, render=render)
# Save rollouts
data = {'rollouts': rollouts}
p.append(data)
del data
data_file_name = data_path + '/rollout_data.pkl'
pickle.dump(p, open(data_file_name, "wb"))
# Model update
print("Updating model !!\n")
model.updateModel(rollouts, pi)
print("Model graph:", model.transitionGraph.nodes)
print("Model options:", model.transitionGraph.edges)
edges = list(model.transitionGraph.edges)
for i in range(0, len(edges)):
print(edges[i][0], " -> ", edges[i][1], " : ", model.transitionGraph[edges[i][0]][edges[i][1]]['weight'])
datas = [0 for _ in range(num_options)]
add_vtarg_and_adv(rollouts, pi, gamma, lam, num_options)
ob, ac, opts, atarg, tdlamret = rollouts["seg_obs"], rollouts["seg_acs"], rollouts["des_opts"], rollouts["adv"], rollouts["tdlamret"]
old_opts = rollouts["seg_opts"]
similarity = 0
for i in range(0, len(old_opts)):
if old_opts[i] == opts[i]:
similarity += 1
print("Percentage similarity of options: ", similarity/len(old_opts) * 100)
vpredbefore = rollouts["vpreds"] # 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
assign_old_eq_new()
pi.eps = pi.eps * gamma #reduce exploration
# Optimizing the policy
print("\nOptimizing policy !! \n")
for opt in range(num_options):
indices = np.where(opts == opt)[0]
print("Option- ", opt, " Batch Size: ", indices.size)
if not indices.size:
continue
datas[opt] = d = Dataset(dict(ob=ob[indices], ac=ac[indices], atarg=atarg[indices], vtarg=tdlamret[indices]), shuffle=not pi.recurrent)
if indices.size < optim_batchsize:
print("Too few samples for opt - ", opt)
continue
optim_batchsize_corrected = optim_batchsize
optim_epochs_corrected = np.clip(np.int(indices.size / optim_batchsize_corrected), 1, optim_epochs)
print("Optim Epochs:", optim_epochs_corrected)
logger.log("Optimizing...")
# Here we do a bunch of optimization epochs over the data
for _ in range(optim_epochs_corrected):
losses = [] # list of tuples, each of which gives the loss for a minibatch
for batch in d.iterate_once(optim_batchsize_corrected):
*newlosses, grads = lossandgrad(batch["ob"], batch["ac"], batch["atarg"], batch["vtarg"], cur_lrmult, [opt])
if np.isnan(newlosses).any():
continue
adam.update(grads, optim_stepsize * cur_lrmult)
losses.append(newlosses)
if len(losses) > 0:
meanlosses, _, _ = mpi_moments(losses, axis=0)
print("Mean loss ", meanlosses)
for (lossval, name) in zipsame(meanlosses, loss_names):
logger.record_tabular("loss_" + name, lossval)
lrlocal = (rollouts["ep_lens"], rollouts["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("Success", rollouts["success"])
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 model_path and not retrain:
U.save_state(model_path + '/')
model_file_name = model_path + '/hybrid_model.pkl'
pickle.dump(model, open(model_file_name, "wb"), pickle.HIGHEST_PROTOCOL)
print("Policy and Model saved in - ", model_path)
'''
return pi, model
def learn(encoder,
action_decorder,
state_decorder,
embedding_shape,
*,
dataset,
logdir,
batch_size,
time_steps,
epsilon=0.001,
lr_rate=1e-3):
lstm_encoder = encoder("lstm_encoder")
ac_decoder = action_decorder("ac_decoder")
state_decoder = state_decorder("state_decoder") #换成了mlp
obs = U.get_placeholder_cached(name="obs") ##for encoder
ob = U.get_placeholder_cached(name="ob")
embedding = U.get_placeholder_cached(name="embedding")
# obss = U.get_placeholder_cached(name="obss") ## for action decoder， 这个state decoder是不是也可以用, 是不是应该改成obs
# ## for action decoder, 这个state decoder应该也是可以用的
# embeddingss = U.get_placeholder_cached(name="embeddingss")
ac = ac_decoder.pdtype.sample_placeholder([None])
obs_out = state_decoder.pdtype.sample_placeholder([None])
# p(z) 标准正太分布, state先验分布？？？是不是应该换成demonstration的标准正态分布？？？？ 可以考虑一下这个问题
from common.distributions import make_pdtype
p_z_pdtype = make_pdtype(embedding_shape)
p_z_params = U.concatenate([
tf.zeros(shape=[embedding_shape], name="mean"),
tf.zeros(shape=[embedding_shape], name="logstd")
],
axis=-1)
p_z = p_z_pdtype.pdfromflat(p_z_params)
recon_loss = -tf.reduce_mean(
tf.reduce_sum(ac_decoder.pd.logp(ac) + state_decoder.pd.logp(obs_out),
axis=0)) ##这个地方还要再改
kl_loss = lstm_encoder.pd.kl(p_z) ##p(z)：标准正太分布, 这个看起来是不是也不太对！！！！
vae_loss = recon_loss + kl_loss ###vae_loss 应该是一个batch的
ep_stats = stats(["recon_loss", "kl_loss", "vae_loss"])
losses = [recon_loss, kl_loss, vae_loss]
## var_list
var_list = []
en_var_list = lstm_encoder.get_trainable_variables()
var_list.extend(en_var_list)
# ac_de_var_list = ac_decoder.get_trainable_variables()
# var_list.extend(ac_de_var_list)
state_de_var_list = state_decoder.get_trainable_variables()
var_list.extend(state_de_var_list)
# compute_recon_loss = U.function([ob, obs, embedding, obss, embeddingss, ac, obs_out], recon_loss)
compute_losses = U.function([obs, ob, embedding, ac, obs_out], losses)
compute_grad = U.function([obs, ob, embedding, ac, obs_out],
U.flatgrad(vae_loss,
var_list)) ###这里没有想好！！！，可能是不对的！！
adam = MpiAdam(var_list, epsilon=epsilon)
U.initialize()
adam.sync()
writer = U.FileWriter(logdir)
writer.add_graph(tf.get_default_graph())
# =========================== TRAINING ===================== #
iters_so_far = 0
saver = tf.train.Saver(var_list=tf.trainable_variables(), max_to_keep=100)
saver_encoder = tf.train.Saver(var_list=en_var_list, max_to_keep=100)
# saver_pol = tf.train.Saver(var_list=ac_de_var_list, max_to_keep=100) ##保留一下policy的参数，但是这个好像用不到哎
while True:
logger.log("********** Iteration %i ************" % iters_so_far)
recon_loss_buffer = deque(maxlen=100)
kl_loss_buffer = deque(maxlen=100)
vae_loss_buffer = deque(maxlen=100)
for observations in dataset.get_next_batch(batch_size=time_steps):
observations = observations.transpose((1, 0))
embedding_now = lstm_encoder.get_laten_vector(observations)
embeddings = np.array([embedding_now for _ in range(time_steps)])
embeddings_reshape = embeddings.reshape((time_steps, -1))
actions = ac_decoder.act(stochastic=True,
ob=observations,
embedding=embeddings_reshape)
state_outputs = state_decoder.get_outputs(
observations.reshape(time_steps, -1, 1),
embeddings) ##还没有加混合高斯......乱加了一通，已经加完了
recon_loss, kl_loss, vae_loss = compute_losses(
observations, observations.reshape(batch_size, time_steps,
-1), embeddings_reshape,
observations.reshape(time_steps, -1, 1), embeddings, actions,
state_outputs)
g = compute_grad(observations,
observations.reshape(batch_size, time_steps,
-1), embeddings_reshape,
observations.reshape(time_steps, -1, 1),
embeddings, actions, state_outputs)
adam.update(g, lr_rate)
recon_loss_buffer.append(recon_loss)
kl_loss_buffer.append(kl_loss)
vae_loss_buffer.append(vae_loss)
ep_stats.add_all_summary(writer, [
np.mean(recon_loss_buffer),
np.mean(kl_loss_buffer),
np.mean(vae_loss_buffer)
], iters_so_far)
logger.record_tabular("recon_loss", recon_loss)
logger.record_tabular("kl_loss", kl_loss)
logger.record_tabular("vae_loss", vae_loss)
logger.dump_tabular()
if (iters_so_far % 10 == 0 and iters_so_far != 0):
save(saver=saver,
sess=tf.get_default_session(),
logdir=logdir,
step=iters_so_far)
save(saver=saver_encoder,
sess=tf.get_default_session(),
logdir="./vae_saver",
step=iters_so_far)
# save(saver=saver_pol, sess=tf.get_default_session(), logdir="pol_saver", step=iters_so_far)
iters_so_far += 1
def learn(env, encoder, action_decorder, state_decorder, embedding_shape,*, dataset, optimizer, logdir, batch_size, time_steps, adam_epsilon = 0.001, lr_rate = 1e-4, vae_beta = 8):
lstm_encoder = encoder("lstm_encoder")
ac_decoder = action_decorder("ac_decoder")
state_decoder = state_decorder("state_decoder") #这个地方有问题
ac_de_ob = U.get_placeholder_cached(name="ac_de_ob")
en_ob = U.get_placeholder_cached(name="en_ob") ##for encoder
state_de_ob = U.get_placeholder_cached(name="state_de_ob") ## for action decoder， 这个state decoder是不是也可以用, 是不是应该改成obs
ac_de_embedding = U.get_placeholder_cached(name="ac_de_embedding") ## for action decoder, 这个state decoder应该也是可以用的
state_de_embedding = U.get_placeholder_cached(name="state_de_embedding")
# ac = ac_decoder.pdtype.sample_placeholder([None])
ob_next = tf.placeholder(name="ob_next", shape=[None, ob_shape], dtype=tf.float32)
# ob_next_ac = tf.placeholder(name="ob_next_ac", shape=[ob_shape], dtype=tf.float32)
# obs_out = state_decoder.pdtype.sample_placeholder([None])
# p(z) 标准正太分布
from common.distributions import make_pdtype
p_z_pdtype = make_pdtype(embedding_shape)
p_z_params = U.concatenate([tf.zeros(shape=[embedding_shape], name="mean"), tf.zeros(shape=[embedding_shape], name="logstd")], axis=-1)
p_z = p_z_pdtype.pdfromflat(p_z_params)
# recon_loss 里再加一个,对于action的
recon_loss = -tf.reduce_sum(state_decoder.pd.logp(ob_next))
# kl_loss = lstm_encoder.pd.kl(p_z)[0] ##p(z)：标准正太分布, 这个看起来是不是也不太对！！！！
# kl_loss = tf.maximum(lstm_encoder.pd.kl(p_z)[0], tf.constant(5.00)) ##p(z)：标准正太分布, 这个看起来是不是也不太对！！！！
kl_loss = lstm_encoder.pd.kl(p_z)[0]
vae_loss = tf.reduce_mean(recon_loss + vae_beta * kl_loss) ###vae_loss 应该是一个batch的
ep_stats = stats(["recon_loss", "kl_loss", "vae_loss"])
losses = [recon_loss, kl_loss, vae_loss]
# 均方误差去训练 action,把得到的action step 一下,得到x(t+1),然后用均方误差loss,或者可以试试交叉熵
## var_list
var_list = []
en_var_list = lstm_encoder.get_trainable_variables()
var_list.extend(en_var_list)
# ac_de_var_list = ac_decoder.get_trainable_variables()
# var_list.extend(ac_de_var_list)
state_de_var_list = state_decoder.get_trainable_variables()
var_list.extend(state_de_var_list)
# compute_recon_loss = U.function([ob, obs, embedding, obss, embeddingss, ac, obs_out], recon_loss)
compute_losses = U.function([en_ob, ac_de_ob, state_de_ob, ac_de_embedding, state_de_embedding, ob_next], losses)
compute_grad = U.function([en_ob, ac_de_ob, state_de_ob, ac_de_embedding, state_de_embedding, ob_next], U.flatgrad(vae_loss, var_list)) ###这里没有想好！！！，可能是不对的！！
adam = MpiAdam(var_list, epsilon=adam_epsilon)
U.initialize()
adam.sync()
writer = U.FileWriter(logdir)
writer.add_graph(tf.get_default_graph())
# =========================== TRAINING ===================== #
iters_so_far = 0
saver = tf.train.Saver(var_list=var_list, max_to_keep=100)
saver_encoder = tf.train.Saver(var_list = en_var_list, max_to_keep=100)
# saver_pol = tf.train.Saver(var_list=ac_de_var_list, max_to_keep=100) ##保留一下policy的参数，但是这个好像用不到哎
while iters_so_far < 50:
## 加多轮
logger.log("********** Iteration %i ************" % iters_so_far)
## 要不要每一轮调整一下batch_size
recon_loss_buffer = deque(maxlen=100)
# recon_loss2_buffer = deque(maxlen=100)
kl_loss_buffer = deque(maxlen=100)
vae_loss_buffer = deque(maxlen=100)
# i = 0
for obs_and_next in dataset.get_next_batch(batch_size=time_steps):
# print(i)
# i += 1
observations = obs_and_next[0].transpose((1, 0))[:-1]
ob_next = obs_and_next[0].transpose(1, 0)[state_decoder.receptive_field:, :]
embedding_now = lstm_encoder.get_laten_vector(obs_and_next[0].transpose((1, 0)))
embeddings = np.array([embedding_now for _ in range(time_steps - 1)])
embeddings_reshape = embeddings.reshape((time_steps-1, -1))
actions = ac_decoder.act(stochastic=True, ob=observations, embedding=embeddings_reshape)
ob_next_ac = get_ob_next_ac(env, observations[-1], actions[0]) ##这个还需要再修改 #########################################3
# state_outputs = state_decoder.get_outputs(observations.reshape(1, time_steps, -1), embedding_now.reshape((1, 1, -1))) ##还没有加混合高斯......乱加了一通，已经加完了
# recon_loss = state_decoder.recon_loss(observations.reshape(1, time_steps, -1), embedding_now.reshape((1, 1, -1)))
recon_loss, kl_loss, vae_loss = compute_losses(obs_and_next[0].transpose((1, 0)).reshape(1, time_steps, -1), observations.reshape(time_steps-1,-1),
observations.reshape(1, time_steps-1, -1), embeddings_reshape, embedding_now.reshape((1,1, -1)), ob_next)
g = compute_grad(obs_and_next[0].transpose((1, 0)).reshape(1, time_steps, -1), observations.reshape(time_steps-1,-1),
observations.reshape(1, time_steps-1, -1), embeddings_reshape, embedding_now.reshape((1,1, -1)), ob_next)
# logger.record_tabular("recon_loss", recon_loss)
# logger.record_tabular("recon_loss2", recon_loss2)
# logger.record_tabular("kl_loss", kl_loss)
# logger.record_tabular("vae_loss", vae_loss)
# logger.dump_tabular()
adam.update(g, lr_rate)
recon_loss_buffer.append(recon_loss)
# recon_loss2_buffer.append(recon_loss2)
kl_loss_buffer.append(kl_loss)
vae_loss_buffer.append(vae_loss)
ep_stats.add_all_summary(writer, [np.mean(recon_loss_buffer), np.mean(kl_loss_buffer),
np.mean(vae_loss_buffer)], iters_so_far)
logger.record_tabular("recon_loss", recon_loss)
# logger.record_tabular("recon_loss2", recon_loss2)
logger.record_tabular("kl_loss", kl_loss)
logger.record_tabular("vae_loss", vae_loss)
logger.dump_tabular()
if(iters_so_far % 10 == 0 and iters_so_far != 0):
save(saver=saver, sess=tf.get_default_session(), logdir=logdir, step=iters_so_far)
save(saver=saver_encoder, sess=tf.get_default_session(),logdir="./vae_saver", step=iters_so_far)
# save(saver=saver_pol, sess=tf.get_default_session(), logdir="pol_saver", step=iters_so_far)
iters_so_far += 1
if iters_so_far < 6:
lr_rate /= 2
def learn(
env,
policy_func,
discriminator,
expert_dataset,
timesteps_per_batch,
*,
g_step,
d_step, # 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,
d_stepsize=3e-4,
schedule='constant', # annealing for stepsize parameters (epsilon and adam)
save_per_iter=100,
ckpt_dir=None,
task="train",
sample_stochastic=True,
load_model_path=None,
task_name=None,
max_sample_traj=1500):
nworkers = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
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
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
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)) # 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]
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)])
d_adam = MpiAdam(discriminator.get_trainable_variables())
adam = MpiAdam(var_list, epsilon=adam_epsilon)
get_flat = U.GetFlat(var_list)
set_from_flat = U.SetFromFlat(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, ret, lrmult], losses)
U.initialize()
th_init = get_flat()
MPI.COMM_WORLD.Bcast(th_init, root=0)
set_from_flat(th_init)
d_adam.sync()
adam.sync()
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
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi,
env,
discriminator,
timesteps_per_batch,
stochastic=True)
traj_gen = traj_episode_generator(pi,
env,
timesteps_per_batch,
stochastic=sample_stochastic)
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
true_rewbuffer = deque(maxlen=100)
assert sum(
[max_iters > 0, max_timesteps > 0, max_episodes > 0,
max_seconds > 0]) == 1, "Only one time constraint permitted"
if task == 'sample_trajectory':
# not elegant, i know :(
sample_trajectory(load_model_path, max_sample_traj, traj_gen,
task_name, sample_stochastic)
sys.exit()
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
# Save model
if iters_so_far % save_per_iter == 0 and ckpt_dir is not None:
U.save_state(os.path.join(ckpt_dir, task_name),
counter=iters_so_far)
logger.log("********** Iteration %i ************" % iters_so_far)
for _ in range(g_step):
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)
# ------------------ Update D ------------------
logger.log("Optimizing Discriminator...")
logger.log(fmt_row(13, discriminator.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
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 discriminator
if hasattr(discriminator, "obs_rms"):
discriminator.obs_rms.update(
np.concatenate((ob_batch, ob_expert), 0))
*newlosses, g = discriminator.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)))
# ----------------- logger --------------------
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_true_rets"]
) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews, true_rews = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
true_rewbuffer.extend(true_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 MPI.COMM_WORLD.Get_rank() == 0:
logger.dump_tabular()
def learn(
env,
policy_func,
*,
timesteps=4,
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)
save_per_iter=100,
ckpt_dir=None,
task="train",
sample_stochastic=True,
load_model_path=None,
task_name=None,
max_sample_traj=1500):
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_func("pi", timesteps, ob_space,
ac_space) # Construct network for new policy
oldpi = policy_func("oldpi", timesteps, 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
pi_vpred = tf.placeholder(dtype=tf.float32, shape=[None])
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")
# ob_now = tf.placeholder(dtype=tf.float32, shape=[optim_batchsize, list(ob_space.shape)[0]])
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
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)) # PPO's pessimistic surrogate (L^CLIP)
vf_loss = U.mean(tf.square(pi.vpred - ret))
# total_loss = pol_surr + pol_entpen + vf_loss
total_loss = pol_surr + pol_entpen
losses = [pol_surr, pol_entpen, meankl, meanent]
loss_names = ["pol_surr", "pol_entpen", "kl", "ent"]
var_list = pi.get_trainable_variables()
vf_var_list = [
v for v in var_list if v.name.split("/")[1].startswith("vf")
]
pol_var_list = [
v for v in var_list if not v.name.split("/")[1].startswith("vf")
]
# lossandgrad = U.function([ob, ac, atarg ,ret, lrmult], losses + [U.flatgrad(total_loss, var_list)])
lossandgrad = U.function([ob, ac, atarg, ret, lrmult],
losses + [U.flatgrad(total_loss, pol_var_list)])
vf_grad = U.function([ob, ac, atarg, ret, lrmult],
U.flatgrad(vf_loss, vf_var_list))
# adam = MpiAdam(var_list, epsilon=adam_epsilon)
pol_adam = MpiAdam(pol_var_list, epsilon=adam_epsilon)
vf_adam = MpiAdam(vf_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()
pol_adam.sync()
vf_adam.sync()
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi,
timesteps,
env,
timesteps_per_batch,
stochastic=True)
traj_gen = traj_episode_generator(pi,
env,
timesteps_per_batch,
stochastic=sample_stochastic)
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
EpRewMean_MAX = 2.5e3
assert sum(
[max_iters > 0, max_timesteps > 0, max_episodes > 0,
max_seconds > 0]) == 1, "Only one time constraint permitted"
if task == 'sample_trajectory':
# not elegant, i know :(
sample_trajectory(load_model_path, max_sample_traj, traj_gen,
task_name, sample_stochastic)
sys.exit()
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
# Save model
if iters_so_far % save_per_iter == 0 and ckpt_dir is not None:
U.save_state(os.path.join(ckpt_dir, task_name),
counter=iters_so_far)
logger.log("********** Iteration %i ************" % iters_so_far)
# if(iters_so_far == 1):
# a = 1
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, vpred, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg[
"vpred"], 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, vpred=vpred, vtarg=tdlamret),
shuffle=False
) #d = Dataset(dict(ob=ob, ac=ac, atarg=atarg, vpred = vpred, 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
pre_obs = [seg["ob_reset"] for jmj in range(timesteps - 1)]
for batch in d.iterate_once(optim_batchsize):
##feed ob, 重新处理一下ob,在batch["ob"]的最前面插入timesteps-1个env.reset的ob,然后滑动串口划分一下batch['ob]
ob_now = np.append(pre_obs, batch['ob']).reshape(
optim_batchsize + timesteps - 1,
list(ob_space.shape)[0])
pre_obs = ob_now[-(timesteps - 1):]
ob_fin = []
for jmj in range(optim_batchsize):
ob_fin.append(ob_now[jmj:jmj + timesteps])
*newlosses, g = lossandgrad(ob_fin, batch["ac"],
batch["atarg"], batch["vtarg"],
cur_lrmult) ###这里的g好像都是0
#adam.update(g, optim_stepsize * cur_lrmult)
pol_adam.update(g, optim_stepsize * cur_lrmult)
vf_g = vf_grad(ob_fin, batch["ac"], batch["atarg"],
batch["vtarg"], cur_lrmult)
vf_adam.update(vf_g, optim_stepsize * cur_lrmult)
losses.append(newlosses)
logger.log(fmt_row(13, np.mean(losses, axis=0)))
pre_obs = [seg["ob_reset"] for jmj in range(timesteps - 1)]
for batch in d.iterate_once(optim_batchsize):
##feed ob, 重新处理一下ob,在batch["ob"]的最前面插入timesteps-1个env.reset的ob,然后滑动串口划分一下batch['ob]
ob_now = np.append(pre_obs, batch['ob']).reshape(
optim_batchsize + timesteps - 1,
list(ob_space.shape)[0])
pre_obs = ob_now[-(timesteps - 1):]
ob_fin = []
for jmj in range(optim_batchsize):
ob_fin.append(ob_now[jmj:jmj + timesteps])
*newlosses, g = lossandgrad(ob_fin, batch["ac"],
batch["atarg"], batch["vtarg"],
cur_lrmult) ###这里的g好像都是0
#adam.update(g, optim_stepsize * cur_lrmult)
pol_adam.update(g, optim_stepsize * cur_lrmult)
vf_g = vf_grad(ob_fin, batch["ac"], batch["atarg"],
batch["vtarg"], cur_lrmult)
vf_adam.update(vf_g, optim_stepsize * cur_lrmult)
logger.log("Evaluating losses...")
losses = []
loss_pre_obs = [seg["ob_reset"] for jmj in range(timesteps - 1)]
for batch in d.iterate_once(optim_batchsize):
### feed ob
ob_now = np.append(loss_pre_obs, batch['ob']).reshape(
optim_batchsize + timesteps - 1,
list(ob_space.shape)[0])
loss_pre_obs = ob_now[-(timesteps - 1):]
ob_fin = []
for jmj in range(optim_batchsize):
ob_fin.append(ob_now[jmj:jmj + timesteps])
newlosses = compute_losses(ob_fin, 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))
if (np.mean(rewbuffer) > EpRewMean_MAX):
EpRewMean_MAX = np.mean(rewbuffer)
print(iters_so_far)
print(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()