def learn(
*,
network,
env,
seed=None,
beta,
total_timesteps,
sil_update,
sil_loss,
timesteps_per_batch=2048, # what to train on
epsilon=0.01,
cg_iters=10,
gamma=0.99,
lam=0.98, # advantage estimation
entcoeff=0.0,
lr=3e-4,
cg_damping=0.1,
vf_stepsize=1e-3,
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,
TRPO=False,
**network_kwargs):
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)
nworkers = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
policy = build_policy(env,
network,
value_network='copy',
copos=True,
**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)
if model_fn is None:
model_fn = Model
discrete_ac_space = isinstance(ac_space, gym.spaces.Discrete)
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=entcoeff,
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=entcoeff,
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()
old_entropy = oldpi.pd.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
entbonus = entcoeff * 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 = 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")]
#vf_var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("vf")]
all_var_list = get_trainable_variables("pi")
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 and fvp???
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)
out = np.empty_like(x)
MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
out /= nworkers
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)
# Initialize eta, omega optimizer
if discrete_ac_space:
init_eta = 1
init_omega = 0.5
eta_omega_optimizer = EtaOmegaOptimizerDiscrete(
beta, epsilon, init_eta, init_omega)
else:
init_eta = 0.5
init_omega = 2.0
#????eta_omega_optimizer details?????
eta_omega_optimizer = EtaOmegaOptimizer(beta, epsilon, init_eta,
init_omega)
# 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
assert sum([max_iters > 0, total_timesteps > 0, max_episodes > 0]) == 1
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, 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
#print(ob[:20])
#print(ac[:20])
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()
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()
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
# Split direction into log-linear 'w_theta' and non-linear 'w_beta' parts
w_theta, w_beta = pi.split_w(copos_update_dir)
tmp_ob = np.zeros(
(1, ) + env.observation_space.shape
) # We assume that entropy does not depend on the NN
# Optimize eta and omega
if discrete_ac_space:
entropy = lossbefore[4]
#entropy = - 1/timesteps_per_batch * np.sum(np.sum(pi.get_action_prob(ob) * pi.get_log_action_prob(ob), axis=1))
eta, omega = eta_omega_optimizer.optimize(
pi.compute_F_w(ob, copos_update_dir),
pi.get_log_action_prob(ob), timesteps_per_batch,
entropy)
else:
Waa, Wsa = pi.w2W(w_theta)
wa = pi.get_wa(ob, w_beta)
varphis = pi.get_varphis(ob)
#old_ent = old_entropy.eval({oldpi.ob: tmp_ob})[0]
old_ent = lossbefore[4]
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()
prev_theta, prev_beta = pi.all_to_theta_beta(
current_theta_beta)
if discrete_ac_space:
# 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, discrete_ac_space)
logger.log("Updated eta, eta: " + str(eta))
set_from_flat(pi.theta_beta_to_all(prev_theta, prev_beta))
# Find proper omega for new eta. Use old policy parameters first.
eta, omega = eta_omega_optimizer.optimize(
pi.compute_F_w(ob, copos_update_dir),
pi.get_log_action_prob(ob), timesteps_per_batch,
entropy, eta)
logger.log("Updated omega, eta: " + str(eta) +
" and omega: " + str(omega))
# do line search for ratio for non-linear "beta" parameter values
#ratio = beta_ratio_line_search(w_theta, w_beta, eta, omega, allmean, compute_losses, get_flat, set_from_flat, pi,
# epsilon, beta, args)
# set ratio to 1 if we do not use beta ratio line search
ratio = 1
#print("ratio from line search: " + str(ratio))
cur_theta = (eta * prev_theta +
w_theta.reshape(-1, )) / (eta + omega)
cur_beta = prev_beta + ratio * w_beta.reshape(-1, ) / eta
else:
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, 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, 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
set_from_flat(pi.theta_beta_to_all(cur_theta, cur_beta))
meanlosses = surr, kl, *_ = allmean(
np.array(compute_losses(*args)))
##copos specific over
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:])
#cg over
for (lossname, lossval) in zip(loss_names, meanlosses):
logger.record_tabular(lossname, lossval)
#policy update over
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
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
print("Reward max: " + str(max(rewbuffer)))
print("Reward min: " + str(min(rewbuffer)))
logger.record_tabular(
"EpLenMean",
np.mean(lenbuffer) if np.sum(lenbuffer) != 0.0 else 0.0)
logger.record_tabular(
"EpRewMean",
np.mean(rewbuffer) if np.sum(rewbuffer) != 0.0 else 0.0)
logger.record_tabular(
"AverageReturn",
np.mean(rewbuffer) if np.sum(rewbuffer) != 0.0 else 0.0)
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 sil_update > 0:
logger.record_tabular("SilSamples", sil_samples)
if rank == 0:
logger.dump_tabular()
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,
method,
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):
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()
old_entropy = oldpi.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)
surrgain = tf.reduce_mean(pi.pd.logp(ac) * 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")
]
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([
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)
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 = 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()
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()
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
# 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.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()
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, get_flat, set_from_flat,
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.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
set_from_flat(pi.theta_beta_to_all(cur_theta, cur_beta))
meanlosses = surr, kl, *_ = allmean(
np.array(compute_losses(*args)))
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)
logger.record_tabular("Name", method)
logger.record_tabular("Iteration", iters_so_far)
logger.record_tabular("trial", trial)
if rank == 0:
logger.dump_tabular()
class LocalTrainer(object):
def __init__(self, id, env, runner, policy, old_policy, pis, global_policy,
config):
self.id = id
self._name = 'local_%d' % id
self._env = env.unwrapped
self._runner = runner
self._config = config
self._policy = policy
self._old_policy = old_policy
self._pis = pis
self._ent_coeff = config.ent_coeff
# set to the global network
self._init_network = U.function(
[],
tf.group(*[
tf.assign(v2, v1)
for v1, v2 in zip(global_policy.var_list, policy.var_list)
]))
# copy weights to the global network
self._copy_network = U.function(
[],
tf.group(*[
tf.assign(v1, v2)
for v1, v2 in zip(global_policy.var_list, policy.var_list)
]))
# tensorboard summary
self._is_chef = (MPI.COMM_WORLD.Get_rank() == 0)
self._num_workers = MPI.COMM_WORLD.Get_size()
self.summary_name = ["reward", "length", "adv"]
self.summary_name += env.unwrapped.reward_type
# build loss/optimizers
self._build_trpo()
if self._config.is_train:
self.summary_name = [
'{}/{}'.format(self._name, key) for key in self.summary_name
]
def init_network(self):
self._init_network()
def copy_network(self):
self._copy_network()
@contextmanager
def timed(self, msg):
if self._is_chef:
print(colorize(msg, color='magenta'))
tstart = time.time()
yield
print(
colorize("done in %.3f seconds" % (time.time() - tstart),
color='magenta'))
else:
yield
def _all_mean(self, x):
assert isinstance(x, np.ndarray)
out = np.empty_like(x)
MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
out /= self._num_workers
return out
def _build_trpo(self):
pi = self._policy
oldpi = self._old_policy
other_pis = self._pis
# input placeholders
ob = pi.ob
ac = pi.pdtype.sample_placeholder([None], name='action')
atarg = tf.placeholder(
dtype=tf.float32, shape=[None],
name='advantage') # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None],
name='return') # Empirical return
# policy
all_var_list = pi.get_trainable_variables()
pol_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")
]
self._vf_adam = MpiAdam(vf_var_list)
kl_oldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
mean_kl = tf.reduce_mean(kl_oldnew)
mean_ent = tf.reduce_mean(ent)
pol_entpen = -self._config.ent_coeff * mean_ent
vf_loss = tf.reduce_mean(tf.square(pi.vpred - ret))
ratio = tf.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac))
pol_surr = tf.reduce_mean(ratio * atarg)
# divergence
other_obs = [] # put id-th data
for other_pi in other_pis:
other_obs.extend(other_pi.obs[self.id])
my_obs_for_other = flatten_lists(pi.obs) # put i-th data
other_obs_for_other = [] # put i-th data
for i, other_pi in enumerate(other_pis):
other_obs_for_other.extend(other_pi.obs[i])
pairwise_divergence = [tf.constant(0.)]
for i, other_pi in enumerate(other_pis):
if i != self.id:
pairwise_divergence.append(
tf.reduce_mean(pi.pds[self.id].kl(other_pi.pds[self.id])))
pairwise_divergence.append(
tf.reduce_mean(other_pi.pds[i].kl(pi.pds[i])))
pol_divergence = self._config.divergence_coeff * tf.reduce_mean(
pairwise_divergence)
pol_loss = pol_surr + pol_entpen + pol_divergence
pol_losses = {
'pol_loss': pol_loss,
'pol_surr': pol_surr,
'pol_entpen': pol_entpen,
'pol_divergence': pol_divergence,
'kl': mean_kl,
'entropy': mean_ent
}
self.summary_name += ['vf_loss']
self.summary_name += pol_losses.keys()
self._get_flat = U.GetFlat(pol_var_list)
self._set_from_flat = U.SetFromFlat(pol_var_list)
klgrads = tf.gradients(mean_kl, pol_var_list)
flat_tangent = tf.placeholder(dtype=tf.float32,
shape=[None],
name="flat_tan")
shapes = [var.get_shape().as_list() for var in pol_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, pol_var_list)
self._update_oldpi = U.function(
[], [],
updates=[
tf.assign(oldv, newv) for (
oldv,
newv) in zipsame(oldpi.get_variables(), pi.get_variables())
])
obs_pairwise = other_obs + my_obs_for_other + other_obs_for_other + ob
self._compute_losses = U.function(obs_pairwise + [ac, atarg],
pol_losses)
pol_losses = dict(pol_losses)
pol_losses.update({'g': U.flatgrad(pol_loss, pol_var_list)})
self._compute_lossandgrad = U.function(obs_pairwise + [ac, atarg],
pol_losses)
self._compute_fvp = U.function([flat_tangent] + obs_pairwise +
[ac, atarg], fvp)
self._compute_vflossandgrad = U.function(
ob + [ret], U.flatgrad(vf_loss, vf_var_list))
self._compute_vfloss = U.function(ob + [ret], vf_loss)
# initialize and sync
U.initialize()
th_init = self._get_flat()
MPI.COMM_WORLD.Bcast(th_init, root=0)
self._set_from_flat(th_init)
self._vf_adam.sync()
rank = MPI.COMM_WORLD.Get_rank()
if self._config.debug:
logger.log(
"[worker: {} local net: {}] Init pol param sum: {}".format(
rank, self.id, th_init.sum()))
logger.log(
"[worker: {} local net: {}] Init vf param sum: {}".format(
rank, self.id,
self._vf_adam.getflat().sum()))
def generate_rollout(self, sess, context=None):
with sess.as_default(), sess.graph.as_default():
with self.timed("sampling"):
rollout = self._runner.rollout(stochastic=True,
context=context)
self._runner.add_advantage(rollout, 0.99, 0.98)
self.rollout = rollout
def update(self, sess, rollouts, global_step):
config = self._config
with sess.as_default(), sess.graph.as_default():
# train policy
info = self._update_policy(rollouts, global_step)
for key, value in rollouts[self.id].items():
if key.startswith('ep_'):
info[key.split('ep_')[1]] = np.mean(value)
if self._is_chef:
logger.log(
'[worker {}] iter: {}, rewards: {}, length: {}'.format(
self.id, global_step, np.mean(info["reward"]),
np.mean(info["length"])))
info = {
'{}/{}'.format(self._name, key): np.mean(value)
for key, value in info.items()
}
return info
def evaluate(self, ckpt_num=None, record=False, context=None):
config = self._config
ep_lens = []
ep_rets = []
if record:
record_dir = osp.join(config.log_dir, 'video')
os.makedirs(record_dir, exist_ok=True)
for _ in tqdm.trange(5):
ep_traj = self._runner.rollout(True, True, context)
ep_lens.append(ep_traj["ep_length"][0])
ep_rets.append(ep_traj["ep_reward"][0])
logger.log('[{}] Trial #{}: lengths {}, returns {}'.format(
self._name, _, ep_traj["ep_length"][0],
ep_traj["ep_reward"][0]))
# Video recording
if record:
visual_obs = ep_traj["visual_ob"]
video_name = '{}{}_{}{}.{}'.format(
config.video_prefix or '', self._name,
'' if ckpt_num is None else 'ckpt_{}_'.format(ckpt_num), _,
config.video_format)
video_path = osp.join(record_dir, video_name)
if config.video_format == 'mp4':
fps = 60.
def f(t):
frame_length = len(visual_obs)
new_fps = 1. / (1. / fps + 1. / frame_length)
idx = min(int(t * new_fps), frame_length - 1)
return visual_obs[idx]
video = mpy.VideoClip(f,
duration=len(visual_obs) / fps + 2)
video.write_videofile(video_path, fps, verbose=False)
elif config.video_format == 'gif':
imageio.mimsave(video_path, visual_obs, fps=100)
logger.log('[{}] Episode Length: {}'.format(self._name,
np.mean(ep_lens)))
logger.log('[{}] Episode Rewards: {}'.format(self._name,
np.mean(ep_rets)))
def update_ob_rms(self, rollouts):
assert self._config.obs_norm == 'learn'
ob = np.concatenate([rollout['ob'] for rollout in rollouts])
ob_dict = self._env.get_ob_dict(ob)
for ob_name in self._policy.ob_type:
self._policy.ob_rms[ob_name].update(ob_dict[ob_name])
def _update_policy(self, rollouts, it):
pi = self._policy
seg = rollouts[self.id]
info = defaultdict(list)
ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg[
"tdlamret"]
atarg = (atarg - atarg.mean()) / atarg.std()
info['adv'] = np.mean(atarg)
other_ob_list = []
for i, other_pi in enumerate(self._pis):
other_ob_list.extend(other_pi.get_ob_list(rollouts[i]["ob"]))
ob_list = pi.get_ob_list(ob)
args = ob_list * self._config.num_contexts + \
other_ob_list * 2 + ob_list + [ac, atarg]
fvpargs = [arr[::5] for arr in args]
def fisher_vector_product(p):
return self._all_mean(self._compute_fvp(
p, *fvpargs)) + self._config.cg_damping * p
self._update_oldpi()
with self.timed("compute gradient"):
lossbefore = self._compute_lossandgrad(*args)
lossbefore = {
k: self._all_mean(np.array(lossbefore[k]))
for k in sorted(lossbefore.keys())
}
g = lossbefore['g']
if np.allclose(g, 0):
logger.log("Got zero gradient. not updating")
else:
with self.timed("compute conjugate gradient"):
stepdir = cg(fisher_vector_product,
g,
cg_iters=self._config.cg_iters,
verbose=False)
assert np.isfinite(stepdir).all()
shs = .5 * stepdir.dot(fisher_vector_product(stepdir))
lm = np.sqrt(shs / self._config.max_kl)
fullstep = stepdir / lm
expectedimprove = g.dot(fullstep)
surrbefore = lossbefore['pol_loss']
stepsize = 1.0
thbefore = self._get_flat()
for _ in range(10):
thnew = thbefore + fullstep * stepsize
self._set_from_flat(thnew)
meanlosses = self._compute_losses(*args)
meanlosses = {
k: self._all_mean(np.array(meanlosses[k]))
for k in sorted(meanlosses.keys())
}
for key, value in meanlosses.items():
if key != 'g':
info[key].append(value)
surr = meanlosses['pol_loss']
kl = meanlosses['kl']
meanlosses = np.array(list(meanlosses.values()))
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._config.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._num_workers > 1 and it % 20 == 0:
paramsums = MPI.COMM_WORLD.allgather(
(thnew.sum(),
self._vf_adam.getflat().sum())) # list of tuples
assert all(
np.allclose(ps, paramsums[0])
for ps in paramsums[1:]), paramsums
with self.timed("updating value function"):
for _ in range(self._config.vf_iters):
for (mbob, mbret) in dataset.iterbatches(
(ob, tdlamret),
include_final_partial_batch=False,
batch_size=self._config.vf_batch_size):
ob_list = pi.get_ob_list(mbob)
g = self._all_mean(
self._compute_vflossandgrad(*ob_list, mbret))
self._vf_adam.update(g, self._config.vf_stepsize)
vf_loss = self._all_mean(
np.array(self._compute_vfloss(*ob_list, mbret)))
info['vf_loss'].append(vf_loss)
for key, value in info.items():
info[key] = np.mean(value)
return info
def learn(
*,
network,
env,
eval_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,
log_path=None,
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
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 = 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
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=pi, logdir=logdir)
max_inner_iter = 500000 if env.spec.id == 'InvertedDoublePendulum-v2' else 3000000
epoch = vf_iters
batch_size = timesteps_per_batch
mb_size = 256
inner_iter_per_iter = epoch * int(batch_size / mb_size)
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 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'
for update in range(1, max_iter + 1):
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)
if (update - 1) % eval_interval == 0:
evaluator.run_evaluation(update - 1)
if (update - 1) % save_interval == 0:
ckpt = tf.train.Checkpoint(model=pi)
ckpt.save(modeldir + '/ckpt_ite' + str((update - 1)))
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=mb_size):
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"]) # 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(
env,
policy_fn,
*,
timesteps_per_batch, # what to train on
max_kl,
cg_iters,
gamma,
lam, # advantage estimation
entc=0.5,
cg_damping=1e-2,
vf_stepsize=3e-4,
vf_iters=3,
max_timesteps=0,
max_episodes=0,
max_iters=0, # time constraint
callback=None,
i_trial):
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])
entp = tf.placeholder(dtype=tf.float32, shape=[])
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
entbonus = entp * 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", "loss_ent"]
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([
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, entp], losses)
compute_lossandgrad = U.function([ob, ac, atarg, entp], 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()
tf.global_variables_initializer()
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,
gamma=gamma)
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
drwdsbuffer = deque(maxlen=40)
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)
# entcoeff = max(entc - float(iters_so_far) / float(max_iters), 0.01)
entcoeff = 0.0
# 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, entcoeff)
lossbefore = allmean(np.array(lossbefore))
g = allmean(g)
if np.allclose(g, 0):
print("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, entcoeff)))
improve = surr - surrbefore
print("Expected: %.3f Actual: %.3f" %
(expectedimprove, improve))
if not np.isfinite(meanlosses).all():
print("Got non-finite value of losses -- bad!")
elif kl > max_kl * 1.5:
print("violated KL constraint. shrinking step.")
elif improve < 0:
print("surrogate didn't improve. shrinking step.")
else:
print("Stepsize OK!")
break
stepsize *= .5
else:
print("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.logkv(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.logkv("ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
lrlocal = (seg["ep_lens"], seg["ep_rets"], seg["ep_drwds"]
) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews, drwds = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
drwdsbuffer.extend(drwds)
logger.logkv("EpLenMean", np.mean(lenbuffer))
logger.logkv("EpRewMean", np.mean(rewbuffer))
logger.logkv("EpThisIter", len(lens))
logger.logkv("EpDRewMean", np.mean(drwdsbuffer))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
logger.logkv("EpisodesSoFar", episodes_so_far)
logger.logkv("TimestepsSoFar", timesteps_so_far)
logger.logkv("TimeElapsed", time.time() - tstart)
logger.logkv('trial', i_trial)
logger.logkv("Iteration", iters_so_far)
logger.logkv("Name", 'TRPO')
if rank == 0:
logger.dump_tabular()
def learn(
env,
policy_func,
reward_giver,
expert_dataset,
rank,
pretrained,
pretrained_weight,
*,
# 0
g_step,
d_step,
entcoeff,
save_per_iter,
# 1024
ckpt_dir,
log_dir,
timesteps_per_batch,
task_name,
robot_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):
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
# if provide pretrained weight
if pretrained_weight is not None:
U.load_state(pretrained_weight, var_list=pi.get_variables())
if robot_name == 'scara':
summary_writer = tf.summary.FileWriter(
'/home/yue/gym-gazebo/Tensorboard/scara',
graph=tf.get_default_graph())
elif robot_name == 'mara':
# summary_writer=tf.summary.FileWriter('/home/yue/gym-gazebo/Tensorboard/mara/down-home_position',graph=tf.get_default_graph())
summary_writer = tf.summary.FileWriter(
'/home/yue/gym-gazebo/Tensorboard/mara/collisions_model/',
graph=tf.get_default_graph())
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)
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
if nworkers != 1:
g = allmean(compute_vflossandgrad(mbob, mbret))
else:
g = compute_vflossandgrad(mbob, mbret)
vfadam.update(g, vf_stepsize)
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)
if nworkers != 1:
d_adam.update(allmean(g), d_stepsize)
else:
d_adam.update(g, d_stepsize)
d_losses.append(newlosses)
logger.log(fmt_row(13, np.mean(d_losses, axis=0)))
g_loss_summary = tf.Summary(value=[
tf.Summary.Value(tag="g_loss",
simple_value=np.mean(d_losses[0][0]))
])
summary_writer.add_summary(g_loss_summary, timesteps_so_far)
d_loss_summary = tf.Summary(value=[
tf.Summary.Value(tag="d_loss",
simple_value=np.mean(d_losses[0][1]))
])
summary_writer.add_summary(d_loss_summary, timesteps_so_far)
entropy_summary = tf.Summary(value=[
tf.Summary.Value(tag="entropy",
simple_value=np.mean(d_losses[0][2]))
])
summary_writer.add_summary(entropy_summary, timesteps_so_far)
entropy_loss_summary = tf.Summary(value=[
tf.Summary.Value(tag="entropy_loss",
simple_value=np.mean(d_losses[0][3]))
])
summary_writer.add_summary(entropy_loss_summary, timesteps_so_far)
g_acc_summary = tf.Summary(value=[
tf.Summary.Value(tag="g_acc", simple_value=np.mean(d_losses[0][4]))
])
summary_writer.add_summary(g_acc_summary, timesteps_so_far)
expert_acc_summary = tf.Summary(value=[
tf.Summary.Value(tag="expert_acc",
simple_value=np.mean(d_losses[0][5]))
])
summary_writer.add_summary(expert_acc_summary, timesteps_so_far)
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)
summary = tf.Summary(value=[
tf.Summary.Value(tag="MeanDiscriminator",
simple_value=np.mean(rewbuffer))
])
summary_writer.add_summary(summary, timesteps_so_far)
truesummary = tf.Summary(value=[
tf.Summary.Value(tag="MeanGenerator",
simple_value=np.mean(true_rewbuffer))
])
summary_writer.add_summary(truesummary, timesteps_so_far)
true_rets_summary = tf.Summary(value=[
tf.Summary.Value(tag="Generator", simple_value=np.mean(true_rets))
])
summary_writer.add_summary(true_rets_summary, timesteps_so_far)
len_summary = tf.Summary(value=[
tf.Summary.Value(tag="Length", simple_value=np.mean(lenbuffer))
])
summary_writer.add_summary(len_summary, timesteps_so_far)
optimgain_summary = tf.Summary(value=[
tf.Summary.Value(tag="Optimgain",
simple_value=np.mean(meanlosses[0]))
])
summary_writer.add_summary(optimgain_summary, timesteps_so_far)
meankl_summary = tf.Summary(value=[
tf.Summary.Value(tag="Meankl", simple_value=np.mean(meanlosses[1]))
])
summary_writer.add_summary(meankl_summary, timesteps_so_far)
entloss_summary = tf.Summary(value=[
tf.Summary.Value(tag="Entloss",
simple_value=np.mean(meanlosses[2]))
])
summary_writer.add_summary(entloss_summary, timesteps_so_far)
surrgain_summary = tf.Summary(value=[
tf.Summary.Value(tag="Surrgain",
simple_value=np.mean(meanlosses[3]))
])
summary_writer.add_summary(surrgain_summary, timesteps_so_far)
entropy_summary = tf.Summary(value=[
tf.Summary.Value(tag="Entropy",
simple_value=np.mean(meanlosses[4]))
])
summary_writer.add_summary(entropy_summary, timesteps_so_far)
epThisIter_summary = tf.Summary(value=[
tf.Summary.Value(tag="EpThisIter", simple_value=np.mean(len(lens)))
])
summary_writer.add_summary(epThisIter_summary, timesteps_so_far)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("MeanDiscriminator", np.mean(rewbuffer))
# Save model
if robot_name == 'scara':
if iters_so_far % save_per_iter == 0:
if np.mean(rewbuffer) <= 200 or np.mean(
true_rewbuffer) >= -100:
task_name = str(iters_so_far)
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)
if iters_so_far == 2000:
break
elif robot_name == 'mara':
if iters_so_far % save_per_iter == 0:
# if np.mean(rewbuffer) <= 300 or np.mean(true_rewbuffer) >= -400:
task_name = str(iters_so_far)
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)
if iters_so_far == 5000:
break
logger.record_tabular("MeanGenerator", 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()
def learn(env,
policy_func,
reward_giver,
expert_dataset,
rank,
g_step,
d_step,
entcoeff,
save_per_iter,
timesteps_per_batch,
ckpt_dir,
log_dir,
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):
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)
saver = tf.train.Saver(
var_list=tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='pi'))
saver.restore(tf.get_default_session(), U_.getPath() + '/model/bc.ckpt')
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())
# 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"])
# if provide pretrained weight
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)
print('save model as ', fname)
try:
os.makedirs(os.path.dirname(fname))
except OSError:
# folder already exists
pass
saver = tf.train.Saver()
saver.save(tf.get_default_session(), fname)
print("********** Iteration %i ************" % iters_so_far)
def fisher_vector_product(p):
return allmean(compute_fvp(p, *fvpargs)) + cg_damping * p
# ------------------ Update G ------------------
print("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"):
tmp_result = compute_lossandgrad(seg["ob"], seg["ac"], atarg)
lossbefore = tmp_result[:-1]
g = tmp_result[-1]
lossbefore = allmean(np.array(lossbefore))
g = allmean(g)
if np.allclose(g, 0):
print("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)
# print("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 = allmean(
np.array(compute_losses(seg["ob"], seg["ac"], atarg)))
surr = meanlosses[0]
kl = meanlosses[1]
improve = surr - surrbefore
print("Expected: %.3f Actual: %.3f" %
(expectedimprove, improve))
if not np.isfinite(meanlosses).all():
print("Got non-finite value of losses -- bad!")
elif kl > max_kl * 1.5:
print("violated KL constraint. shrinking step.")
elif improve < 0:
print("surrogate didn't improve. shrinking step.")
else:
print("Stepsize OK!")
break
stepsize *= .5
else:
print("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)
print("ev_tdlam_before", explained_variance(vpredbefore, tdlamret))
# ------------------ Update D ------------------
print("Optimizing Discriminator...")
print(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 tqdm(
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))
tmp_result = reward_giver.lossandgrad(ob_batch, ac_batch,
ob_expert, ac_expert)
newlosses = tmp_result[:-1]
g = tmp_result[-1]
d_adam.update(allmean(g), d_stepsize)
d_losses.append(newlosses)
print(fmt_row(13, np.mean(d_losses, axis=0)))
timesteps_so_far += len(seg['ob'])
iters_so_far += 1
print("EpisodesSoFar", episodes_so_far)
print("TimestepsSoFar", timesteps_so_far)
print("TimeElapsed", time.time() - tstart)
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(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,
vf_batchsize=128,
callback=None,
freeze_g=False,
freeze_d=False,
semi_dataset=None,
semi_loss=False):
semi_loss = semi_loss and semi_dataset is not None
l2_w = 0.1
nworkers = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
np.set_printoptions(precision=3)
if rank == 0:
writer = U.file_writer(log_dir)
# print all the hyperparameters in the log...
log_dict = {
"expert trajectories": expert_dataset.num_traj,
"algo": "trpo",
"threads": nworkers,
"timesteps_per_batch": timesteps_per_batch,
"timesteps_per_thread": -(-timesteps_per_batch // nworkers),
"entcoeff": entcoeff,
"vf_iters": vf_iters,
"vf_batchsize": vf_batchsize,
"vf_stepsize": vf_stepsize,
"d_stepsize": d_stepsize,
"g_step": g_step,
"d_step": d_step,
"max_kl": max_kl,
"gamma": gamma,
"lam": lam,
"l2_weight": l2_w
}
if semi_dataset is not None:
log_dict["semi trajectories"] = semi_dataset.num_traj
if hasattr(semi_dataset, 'info'):
log_dict["semi_dataset_info"] = semi_dataset.info
# print them all together for csv
logger.log(",".join([str(elem) for elem in log_dict]))
logger.log(",".join([str(elem) for elem in log_dict.values()]))
# also print them separately for easy reading:
for elem in log_dict:
logger.log(str(elem) + ": " + str(log_dict[elem]))
# divide the timesteps to the threads
timesteps_per_batch = -(-timesteps_per_batch // nworkers
) # get ceil of division
# Setup losses and stuff
# ----------------------------------------
if semi_dataset:
ob_space = semi_ob_space(env, semi_size=semi_dataset.semi_size)
else:
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_func("pi",
ob_space=ob_space,
ac_space=ac_space,
reuse=(pretrained_weight is not None))
oldpi = policy_func("oldpi", ob_space=ob_space, ac_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"]
vf_losses = [vferr]
vf_loss_names = ["vf_loss"]
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/vf")]
assert len(var_list) == len(vf_var_list) + 1
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_vf_losses = U.function([ob, ac, atarg, ret], losses + vf_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], vf_losses +
[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
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)
success_buffer = deque(maxlen=40)
l2_rewbuffer = deque(
maxlen=40) if semi_loss and semi_dataset is not None else None
total_rewbuffer = deque(
maxlen=40) if semi_loss and semi_dataset is not None else None
assert sum([max_iters > 0, max_timesteps > 0, max_episodes > 0]) == 1
not_update = 1 if not freeze_d else 0 # do not update G before D the first time
# if provide pretrained weight
loaded = False
if not U.load_checkpoint_variables(pretrained_weight):
if U.load_checkpoint_variables(pretrained_weight,
check_prefix=get_il_prefix()):
if rank == 0:
logger.log("loaded checkpoint variables from " +
pretrained_weight)
loaded = True
else:
loaded = True
if loaded:
not_update = 0 if any(
[x.op.name.find("adversary") != -1
for x in U.ALREADY_INITIALIZED]) else 1
if pretrained_weight and pretrained_weight.rfind("iter_") and \
pretrained_weight[pretrained_weight.rfind("iter_") + len("iter_"):].isdigit():
curr_iter = int(
pretrained_weight[pretrained_weight.rfind("iter_") +
len("iter_"):]) + 1
print("loaded checkpoint at iteration: " + str(curr_iter))
iters_so_far = curr_iter
timesteps_so_far = iters_so_far * timesteps_per_batch
d_adam = MpiAdam(reward_giver.get_trainable_variables())
vfadam = MpiAdam(vf_var_list)
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,
semi_dataset=semi_dataset,
semi_loss=semi_loss) # ADD L2 loss to semi trajectories
g_loss_stats = stats(loss_names + vf_loss_names)
d_loss_stats = stats(reward_giver.loss_name)
ep_names = ["True_rewards", "Rewards", "Episode_length", "Success"]
if semi_loss and semi_dataset is not None:
ep_names.append("L2_loss")
ep_names.append("total_rewards")
ep_stats = stats(ep_names)
if rank == 0:
start_time = time.time()
ch_count = 0
env_type = env.env.env.__class__.__name__
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)
if env_type.find(
"Pendulum"
) != -1 or save_per_iter != 1: # allow pendulum to save all iterations
fname = os.path.join(ckpt_dir, 'iter_' + str(iters_so_far),
'iter_' + str(iters_so_far))
os.makedirs(os.path.dirname(fname), exist_ok=True)
saver = tf.train.Saver()
saver.save(tf.get_default_session(), fname, write_meta_graph=False)
if rank == 0 and time.time(
) - start_time >= 3600 * ch_count: # save a different checkpoint every hour
fname = os.path.join(ckpt_dir, 'hour' + str(ch_count).zfill(3))
fname = os.path.join(fname, 'iter_' + str(iters_so_far))
os.makedirs(os.path.dirname(fname), exist_ok=True)
saver = tf.train.Saver()
saver.save(tf.get_default_session(), fname, write_meta_graph=False)
ch_count += 1
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 curr_step in range(g_step):
with timed("sampling"):
seg = seg_gen.__next__()
seg["rew"] = seg["rew"] - seg["l2_loss"] * l2_w
add_vtarg_and_adv(seg, gamma, lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
ob, ac, atarg, tdlamret, full_ob = seg["ob"], seg["ac"], seg[
"adv"], seg["tdlamret"], seg["full_ob"]
vpredbefore = seg[
"vpred"] # predicted value function before udpate
atarg = (atarg - atarg.mean()) / atarg.std(
) # standardized advantage function estimate
d = Dataset(dict(ob=full_ob, ac=ac, atarg=atarg, vtarg=tdlamret),
shuffle=True)
if not_update:
break # stop G from updating
if hasattr(pi, "ob_rms"):
pi.ob_rms.update(full_ob) # update running mean/std for policy
args = seg["full_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)))
if rank == 0:
print("Generator entropy " + str(meanlosses[4]) +
", loss " + str(meanlosses[2]))
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"):
logger.log(fmt_row(13, vf_loss_names))
for _ in range(vf_iters):
vf_b_losses = []
for batch in d.iterate_once(vf_batchsize):
mbob = batch["ob"]
mbret = batch["vtarg"]
if hasattr(pi, "ob_rms"):
pi.ob_rms.update(
mbob) # update running mean/std for policy
*newlosses, g = compute_vflossandgrad(mbob, mbret)
g = allmean(g)
newlosses = allmean(np.array(newlosses))
vfadam.update(g, vf_stepsize)
vf_b_losses.append(newlosses)
logger.log(fmt_row(13, np.mean(vf_b_losses, axis=0)))
logger.log("Evaluating losses...")
losses = []
for batch in d.iterate_once(vf_batchsize):
newlosses = compute_vf_losses(batch["ob"], batch["ac"],
batch["atarg"], batch["vtarg"])
losses.append(newlosses)
meanlosses, _, _ = mpi_moments(losses, axis=0)
#########################
'''
For evaluation during training.
Can be commented out for faster training...
'''
for ob_batch, ac_batch, full_ob_batch in dataset.iterbatches(
(ob, ac, full_ob),
include_final_partial_batch=False,
batch_size=len(ob)):
ob_expert, ac_expert = expert_dataset.get_next_batch(
len(ob_batch))
exp_rew = 0
for obs, acs in zip(ob_expert, ac_expert):
exp_rew += 1 - np.exp(
-reward_giver.get_reward(obs, acs)[0][0])
mean_exp_rew = exp_rew / len(ob_expert)
gen_rew = 0
for obs, acs, full_obs in zip(ob_batch, ac_batch,
full_ob_batch):
gen_rew += 1 - np.exp(
-reward_giver.get_reward(obs, acs)[0][0])
mean_gen_rew = gen_rew / len(ob_batch)
if rank == 0:
logger.log("Generator step " + str(curr_step) +
": Dicriminator reward of expert traj " +
str(mean_exp_rew) + " vs gen traj " +
str(mean_gen_rew))
#########################
if not not_update:
g_losses = meanlosses
for (lossname, lossval) in zip(loss_names + vf_loss_names,
meanlosses):
logger.record_tabular(lossname, lossval)
logger.record_tabular("ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
# ------------------ Update D ------------------
if not freeze_d:
logger.log("Optimizing Discriminator...")
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, full_ob_batch in dataset.iterbatches(
(ob, ac, full_ob),
include_final_partial_batch=False,
batch_size=batch_size):
ob_expert, ac_expert = expert_dataset.get_next_batch(
len(ob_batch))
#########################
'''
For evaluation during training.
Can be commented out for faster training...
'''
exp_rew = 0
for obs, acs in zip(ob_expert, ac_expert):
exp_rew += 1 - np.exp(
-reward_giver.get_reward(obs, acs)[0][0])
mean_exp_rew = exp_rew / len(ob_expert)
gen_rew = 0
for obs, acs in zip(ob_batch, ac_batch):
gen_rew += 1 - np.exp(
-reward_giver.get_reward(obs, acs)[0][0])
mean_gen_rew = gen_rew / len(ob_batch)
if rank == 0:
logger.log("Dicriminator reward of expert traj " +
str(mean_exp_rew) + " vs gen traj " +
str(mean_gen_rew))
#########################
# 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))
loss_input = (ob_batch, ac_batch, ob_expert, ac_expert)
*newlosses, g = reward_giver.lossandgrad(*loss_input)
d_adam.update(allmean(g), d_stepsize)
d_losses.append(newlosses)
logger.log(fmt_row(13, reward_giver.loss_name))
logger.log(fmt_row(13, np.mean(d_losses, axis=0)))
lrlocal = (seg["ep_lens"], seg["ep_rets"], seg["ep_true_rets"],
seg["ep_success"], seg["ep_semi_loss"]) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews, true_rets, success, semi_losses = map(
flatten_lists, zip(*listoflrpairs))
# success
success = [
float(elem) for elem in success
if isinstance(elem, (int, float, bool))
] # remove potential None types
if not success:
success = [-1] # set success to -1 if env has no success flag
success_buffer.extend(success)
true_rewbuffer.extend(true_rets)
lenbuffer.extend(lens)
rewbuffer.extend(rews)
if semi_loss and semi_dataset is not None:
semi_losses = [elem * l2_w for elem in semi_losses]
total_rewards = rews
total_rewards = [
re_elem - l2_elem
for re_elem, l2_elem in zip(total_rewards, semi_losses)
]
l2_rewbuffer.extend(semi_losses)
total_rewbuffer.extend(total_rewards)
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("EpSuccess", np.mean(success_buffer))
if semi_loss and semi_dataset is not None:
logger.record_tabular("EpSemiLoss", np.mean(l2_rewbuffer))
logger.record_tabular("EpTotalReward", np.mean(total_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("ItersSoFar", iters_so_far)
if rank == 0:
logger.dump_tabular()
if not not_update:
g_loss_stats.add_all_summary(writer, g_losses, iters_so_far)
if not freeze_d:
d_loss_stats.add_all_summary(writer, np.mean(d_losses, axis=0),
iters_so_far)
# default buffers
ep_buffers = [
np.mean(true_rewbuffer),
np.mean(rewbuffer),
np.mean(lenbuffer),
np.mean(success_buffer)
]
if semi_loss and semi_dataset is not None:
ep_buffers.append(np.mean(l2_rewbuffer))
ep_buffers.append(np.mean(total_rewbuffer))
ep_stats.add_all_summary(writer, ep_buffers, iters_so_far)
if not_update and not freeze_g:
not_update -= 1
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)
init_policy_params=None,
policy_scope='pi'):
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
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
clip_tf = tf.placeholder(dtype=tf.float32)
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_tf,
1.0 + clip_tf * lrmult) * atarg
pol_surr = -tf.reduce_mean(tf.minimum(
surr1, surr2)) # PPO's pessimistic surrogate (L^CLIP)
if hasattr(pi, 'additional_loss'):
pol_surr += pi.additional_loss
vf_loss = tf.reduce_mean(tf.square(pi.vpred - ret))
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()
pol_var_list = [
v for v in pi.get_trainable_variables()
if 'placehold' not in v.name and 'offset' not in v.name and 'secondary'
not in v.name and 'vf' not in v.name and 'pol' in v.name
]
pol_var_size = np.sum([np.prod(v.shape) for v in pol_var_list])
get_pol_flat = U.GetFlat(pol_var_list)
set_pol_from_flat = U.SetFromFlat(pol_var_list)
total_loss = pol_surr + pol_entpen + vf_loss
lossandgrad = U.function([ob, ac, atarg, ret, lrmult, clip_tf],
losses + [U.flatgrad(total_loss, var_list)])
pol_lossandgrad = U.function([ob, ac, atarg, ret, lrmult, clip_tf],
losses +
[U.flatgrad(total_loss, pol_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, clip_tf], losses)
compute_losses_cpo = U.function(
[ob, ac, atarg, ret, lrmult, clip_tf],
[tf.reduce_mean(surr1), pol_entpen, vf_loss, meankl, meanent])
compute_ratios = U.function([ob, ac], ratio)
compute_kls = U.function([ob, ac], kloldnew)
compute_rollout_old_prob = U.function([ob, ac],
tf.reduce_mean(oldpi.pd.logp(ac)))
compute_rollout_new_prob = U.function([ob, ac],
tf.reduce_mean(pi.pd.logp(ac)))
compute_rollout_new_prob_min = U.function([ob, ac],
tf.reduce_min(pi.pd.logp(ac)))
update_ops = {}
update_placeholders = {}
for v in pi.get_trainable_variables():
update_placeholders[v.name] = tf.placeholder(v.dtype,
shape=v.get_shape())
update_ops[v.name] = v.assign(update_placeholders[v.name])
# compute fisher information matrix
dims = [int(np.prod(p.shape)) for p in pol_var_list]
logprob_grad = U.flatgrad(tf.reduce_mean(pi.pd.logp(ac)), pol_var_list)
compute_logprob_grad = U.function([ob, ac], logprob_grad)
U.initialize()
adam.sync()
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('/')]
print(cur_scope, orig_scope)
for i in range(len(pi.get_variables())):
if pi.get_variables()[i].name.replace(cur_scope, orig_scope,
1) in init_policy_params:
assign_op = pi.get_variables()[i].assign(
init_policy_params[pi.get_variables()[i].name.replace(
cur_scope, orig_scope, 1)])
tf.get_default_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)])
tf.get_default_session().run(assign_op)
# 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"
prev_params = {}
for v in var_list:
if 'pol' in v.name:
prev_params[v.name] = v.eval()
optim_seg = None
grad_scale = 1.0
while True:
if MPI.COMM_WORLD.Get_rank() == 0:
print('begin')
memory()
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
unstandardized_adv = np.copy(atarg)
atarg = (atarg - atarg.mean()
) / atarg.std() # standardized advantage function estimate
args = seg["ob"], seg["ac"], atarg
fvpargs = [arr for arr in args]
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))
cur_clip_val = clip_param
d = Dataset(dict(ob=ob, ac=ac, atarg=atarg, vtarg=tdlamret),
shuffle=not pi.recurrent)
for epoch 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, cur_clip_val)
adam.update(g * grad_scale, optim_stepsize * cur_lrmult)
losses.append(newlosses)
logger.log(fmt_row(13, np.mean(losses, axis=0)))
logger.log("Evaluating losses...")
losses = []
d = Dataset(dict(ob=ob, ac=ac, atarg=atarg, vtarg=tdlamret),
shuffle=not pi.recurrent)
for batch in d.iterate_once(optim_batchsize):
newlosses = compute_losses(batch["ob"], batch["ac"],
batch["atarg"], batch["vtarg"],
cur_lrmult, clip_param)
losses.append(newlosses)
meanlosses, _, _ = mpi_moments(losses, axis=0)
if MPI.COMM_WORLD.Get_rank() == 0:
logger.log(fmt_row(13, meanlosses))
for (lossval, name) in zipsame(meanlosses, loss_names):
logger.record_tabular("loss_" + name, lossval)
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)
if MPI.COMM_WORLD.Get_rank() == 0:
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
logger.record_tabular(
"PolVariance",
repr(adam.getflat()[-env.action_space.shape[0]:]))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
if MPI.COMM_WORLD.Get_rank() == 0:
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 MPI.COMM_WORLD.Get_rank() == 0:
print('end')
memory()
return pi, np.mean(rewbuffer)
def learn(env, policy_fn, *,
batch_size, # what to train on
task_horizon,
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,
weights_dir='.',
per_decision = True,
normalize = False,
truncate_at = np.infty
):
nworkers = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
np.set_printoptions(precision=3)
timesteps_per_batch = batch_size * task_horizon
# 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)
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.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([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)
vfadam.sync()
print("Init param sum", th_init.sum(), flush=True)
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi, env, task_horizon, 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)
#Params
#"""
params = pi.eval_param()
#print(params)
np.save(weights_dir+'/weights_'+str(iters_so_far), params)
#"""
# 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)
print('DOT: %s' % np.dot(stepdir, g))
# 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"], seg["ob"],
seg["ac"],seg["rew"]) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews, states, actions, rewards = map(flatten_lists, zip(*listoflrpairs))
disc_rews = []
start = 0
for ep_len in lens:
end = start + ep_len
disc = gamma + np.zeros(ep_len)
disc[0] = 1
disc = np.cumprod(disc)
disc_rewards = np.array(rewards[start:end]) * disc
disc_rews.append(np.sum(disc_rewards))
start = end
#Save importance weights
simple_iw = pi.eval_simple_iw(states,
actions,
rewards,
lens,
gamma=gamma,
behavioral=oldpi)
np.save(weights_dir+'/iws_'+str(iters_so_far), simple_iw)
#print(len(simple_iw), simple_iw)
#Save returns
ep_rets = np.array(disc_rews)
np.save(weights_dir+'/rets_'+str(iters_so_far), ep_rets)
#print(len(ep_rets), ep_rets)
#lenbuffer.extend(lens)
#rewbuffer.extend(rews)
#Renyi
"""
renyi_4 = np.mean(pi.eval_renyi(states, oldpi, 4))
#print('Renyi:', renyi)
#"""
#Importance weights stats
"""
avg_iw, var_iw, max_iw, ess = pi.eval_iw_stats(states,
actions,
rewards,
lens,
gamma=gamma,
behavioral=oldpi,
per_decision=per_decision,
normalize=normalize,
truncate_at=truncate_at)
#"""
#Returns stats
"""
avg_ret, var_ret, max_ret = pi.eval_ret_stats(states,
actions,
rewards,
lens,
gamma=gamma,
behavioral=oldpi,
per_decision=per_decision,
normalize=normalize,
truncate_at=truncate_at)
#"""
#Performance
#"""
bound_delta = .2
batch_size = len(lens)
J = pi.eval_J(states,
actions,
rewards,
lens,
gamma=gamma,
behavioral=oldpi,
per_decision=per_decision,
normalize=normalize,
truncate_at=truncate_at)
var_J = pi.eval_var_J(states,
actions,
rewards,
lens,
gamma=gamma,
behavioral=oldpi,
per_decision=per_decision,
normalize=normalize,
truncate_at=truncate_at)
"""
bound = pi.eval_bound(states,
actions,
rewards,
lens,
gamma=gamma,
behavioral=oldpi,
per_decision=per_decision,
normalize=normalize,
truncate_at=truncate_at,
delta=bound_delta,
use_ess=True)
#"""
#Sample Renyi
d2s = pi.eval_renyi(states, oldpi, 2)
d2s_by_episode = []
start = 0
for ep_len in lens:
end = start + ep_len
d2s_by_episode = np.sum(d2s[start:end])
start = end
sample_d2 = np.mean(np.exp(d2s_by_episode))
"""
grad_bound = pi.eval_grad_bound(states,
actions,
rewards,
lens,
gamma=gamma,
behavioral=oldpi,
per_decision=per_decision,
normalize=normalize,
truncate_at=truncate_at,
delta=bound_delta,
use_ess=True)
print(grad_bound)
#print('Target performance', J, '+-', np.sqrt(var_J/len(lens)))
#"""
#Gradients
"""
grad_J = pi.eval_grad_J(states,
actions,
rewards,
lens,
behavioral=oldpi,
per_decision=True)
grad_var_J = pi.eval_grad_var_J(states,
actions,
rewards,
lens,
behavioral=oldpi,
per_decision=True)
print('Target performance grads', grad_J, grad_var_J)
#"""
#Student-t bound
"""
bound = pi.eval_bound(states,
actions,
rewards,
lens,
behavioral=oldpi,
per_decision=True)
#print('Bound comp. time', time.time() - checkpoint)
print("StudentTBound", bound)
#"""
#Student-t bound grad
"""
bound_grad = pi.eval_bound_grad(states,
actions,
rewards,
lens,
behavioral=oldpi,
per_decision=True)
print("StudentTBound grad", bound_grad)
#"""
#Fisher
"""
checkpoint = time.time()
fisher = oldpi.eval_fisher(states, actions, lens, behavioral=None)
#print(fisher)
assert np.array_equal(fisher, fisher.T)
print('Fisher comp. time', time.time() - checkpoint)
checkpoint = time.time()
natural = np.linalg.solve(fisher + 1e-12*np.eye(fisher.shape[0]), grad_J)
print(natural)
#print('Fisher vector product time:', time.time() - checkpoint)
#"""
#Logging
logger.record_tabular("Step_size", stepsize)
#logger.record_tabular("Our_bound", bound)
#logger.record_tabular("Reny_4", renyi_4)
logger.record_tabular("SampleRenyi2", sample_d2)
#logger.record_tabular("Max_iw", max_iw)
#logger.record_tabular("Ess", ess)
#logger.record_tabular("Avg_iw", avg_iw)
#logger.record_tabular("Var_iw", var_iw)
#logger.record_tabular("Max_ret", max_ret)
#logger.record_tabular("Avg_ret", avg_ret)
#logger.record_tabular("Var_ret", var_ret)
logger.record_tabular("EpLenMean", np.mean(lens))
logger.record_tabular("DiscEpRewMean", np.mean(disc_rews))
logger.record_tabular("EpRewMean", np.mean(rews))
logger.record_tabular("EpThisIter", len(lens))
logger.record_tabular("J_hat", J)
logger.record_tabular("Var_J", var_J)
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_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(
*,
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(
*,
network,
env,
total_timesteps,
timesteps_per_batch=1024, # what to train on
max_kl=0.002,
cg_iters=10,
gamma=0.99,
lam=1.0, # advantage estimation
seed=None,
ent_coef=0.00,
cg_damping=1e-2,
vf_stepsize=3e-4,
vf_iters=3,
max_episodes=0,
max_iters=0, # time constraint
callback=None,
load_path=None,
num_reward=1,
**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))
set_global_seeds(seed)
# 创建policy
policy = build_policy(env,
network,
value_network='copy',
num_reward=num_reward,
**network_kwargs)
process_dir = logger.get_dir()
save_dir = process_dir.split(
'Data')[-2] + 'log/l1/seed' + process_dir[-1] + '/'
os.makedirs(save_dir, exist_ok=True)
coe_save = []
impro_save = []
grad_save = []
adj_save = []
coe = np.ones((num_reward)) / num_reward
np.set_printoptions(precision=3)
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
#################################################################
# ob ac ret atarg 都是 placeholder
# ret atarg 此处应该是向量形式
ob = observation_placeholder(ob_space)
# 创建pi和oldpi
with tf.variable_scope("pi"):
pi = policy(observ_placeholder=ob)
with tf.variable_scope("oldpi"):
oldpi = policy(observ_placeholder=ob)
# 每个reward都可以算一个atarg
atarg = tf.placeholder(
dtype=tf.float32,
shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32,
shape=[None, num_reward]) # Empirical return
ac = pi.pdtype.sample_placeholder([None])
#此处的KL div和entropy与reward无关
##################################
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
# entbonus 是entropy loss
entbonus = ent_coef * meanent
#################################
###########################################################
# vferr 用来更新 v 网络
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 用来更新 policy 网络, 应该每个reward有一个
optimgain = surrgain + entbonus
losses = [optimgain, meankl, entbonus, surrgain, meanent]
loss_names = ["optimgain", "meankl", "entloss", "surrgain", "entropy"]
###########################################################
dist = meankl
# 定义要优化的变量和 V 网络 adam 优化器
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)
# 把变量展开成一个向量的类
get_flat = U.GetFlat(var_list)
# 这个类可以把一个向量分片赋值给var_list里的变量
set_from_flat = U.SetFromFlat(var_list)
# kl散度的梯度
klgrads = tf.gradients(dist, var_list)
####################################################################
# 拉直的向量
flat_tangent = tf.placeholder(dtype=tf.float32,
shape=[None],
name="flat_tan")
# 把拉直的向量重新分成很多向量
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
####################################################################
####################################################################
# 把kl散度梯度与变量乘积相加
gvp = tf.add_n([
tf.reduce_sum(g * tangent)
for (g, tangent) in zipsame(klgrads, tangents)
]) #pylint: disable=E1111
# 把gvp的梯度展成向量
fvp = U.flatgrad(gvp, var_list)
####################################################################
# 用学习后的策略更新old策略
assign_old_eq_new = U.function(
[], [],
updates=[
tf.assign(oldv, newv)
for (oldv,
newv) in zipsame(get_variables("oldpi"), get_variables("pi"))
])
# 计算loss
compute_losses = U.function([ob, ac, atarg], losses)
# 计算loss和梯度
compute_lossandgrad = U.function([ob, ac, atarg], losses +
[U.flatgrad(optimgain, var_list)])
# 计算fvp
compute_fvp = U.function([flat_tangent, ob, ac, atarg], fvp)
# 计算值网络的梯度
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
# 初始化variable
U.initialize()
if load_path is not None:
pi.load(load_path)
# 得到初始化的参数向量
th_init = get_flat()
if MPI is not None:
MPI.COMM_WORLD.Bcast(th_init, root=0)
# 把向量the_init的值分片赋值给var_list
set_from_flat(th_init)
#同步
vfadam.sync()
print("Init param sum", th_init.sum(), flush=True)
# Prepare for rollouts
# ----------------------------------------
# 这是一个生成数据的迭代器
seg_gen = traj_segment_generator(pi,
env,
timesteps_per_batch,
stochastic=True,
num_reward=num_reward)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
# 双端队列
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__()
# 计算累积回报
add_vtarg_and_adv(seg, gamma, lam, num_reward=num_reward)
###########$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ToDo
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
# ob, ac, atarg, tdlamret 的类型都是ndarray
#ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg["tdlamret"]
_, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg[
"tdlamret"]
#print(seg['ob'].shape,type(seg['ob']))
#print(seg['ac'],type(seg['ac']))
#print(seg['adv'],type(seg['adv']))
#print(seg["tdlamret"].shape,type(seg['tdlamret']))
vpredbefore = seg["vpred"] # predicted value function before udpate
# 标准化
#print("============================== atarg =========================================================")
#print(atarg)
atarg = (atarg - np.mean(atarg, axis=0)) / np.std(
atarg, axis=0) # standardized advantage function estimate
#atarg = (atarg) / np.max(np.abs(atarg),axis=0)
#print('======================================= standardized atarg ====================================')
#print(atarg)
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
## set old parameter values to new parameter values
assign_old_eq_new()
G = None
S = None
mr_lossbefore = np.zeros((num_reward, len(loss_names)))
grad_norm = np.zeros((num_reward + 1))
for i in range(num_reward):
args = seg["ob"], seg["ac"], atarg[:, i]
#print(atarg[:,i])
# 算是args的一个sample，每隔5个取出一个
fvpargs = [arr[::5] for arr in args]
# 这个函数计算fisher matrix 与向量 p 的 乘积
def fisher_vector_product(p):
return allmean(compute_fvp(p, *fvpargs)) + cg_damping * p
with timed("computegrad of " + str(i + 1) + ".th reward"):
*lossbefore, g = compute_lossandgrad(*args)
lossbefore = allmean(np.array(lossbefore))
mr_lossbefore[i] = lossbefore
g = allmean(g)
#print("***************************************************************")
#print(g)
#print('==================='+str(i+1)+"=====================",np.linalg.norm(g))
#print(atarg[:,i])
if isinstance(G, np.ndarray):
G = np.vstack((G, g))
else:
G = g
# g是目标函数的梯度
# 利用共轭梯度获得更新方向
if np.allclose(g, 0):
logger.log("Got zero gradient. not updating")
else:
with timed("cg of " + str(i + 1) + ".th reward"):
# stepdir 是更新方向
stepdir = cg(fisher_vector_product,
g,
cg_iters=cg_iters,
verbose=rank == 0)
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
#print(np.linalg.norm(fullstep))
grad_norm[i] = np.linalg.norm(fullstep)
assert np.isfinite(stepdir).all()
if isinstance(S, np.ndarray):
S = np.vstack((S, stepdir))
else:
S = stepdir
#print('======================================= G ====================================')
#print(G)
#print('======================================= S ====================================')
#print(S)
new_coe = get_coefficient(G, S)
#coe = 0.99 * coe + 0.01 * new_coe
coe = new_coe
coe_save.append(coe)
#根据梯度的夹角调整参数
try:
GG = np.dot(S, S.T)
D = np.sqrt(np.diag(1 / np.diag(GG)))
GG = np.dot(np.dot(D, GG), D)
#print('======================================= inner product ====================================')
#print(GG)
adj = np.sum(GG) / (num_reward**2)
except:
adj = 1
#print('======================================= adj ====================================')
#print(adj)
try:
adj = 1
adj_save.append(adj)
adj_max_kl = adj * max_kl
#################################################################
grad_norm = grad_norm * np.sqrt(adj)
stepdir = np.dot(coe, S)
g = np.dot(coe, G)
lossbefore = np.dot(coe, mr_lossbefore)
#################################################################
shs = .5 * stepdir.dot(fisher_vector_product(stepdir))
lm = np.sqrt(shs / adj_max_kl)
# logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g))
fullstep = stepdir / lm
grad_norm[num_reward] = np.linalg.norm(fullstep)
grad_save.append(grad_norm)
expectedimprove = g.dot(fullstep)
surrbefore = lossbefore[0]
stepsize = 1.0
thbefore = get_flat()
def compute_mr_losses():
mr_losses = np.zeros((num_reward, len(loss_names)))
for i in range(num_reward):
args = seg["ob"], seg["ac"], atarg[:, i]
one_reward_loss = allmean(np.array(compute_losses(*args)))
mr_losses[i] = one_reward_loss
mr_loss = np.dot(coe, mr_losses)
return mr_loss, mr_losses
# 做10次搜索
for _ in range(10):
thnew = thbefore + fullstep * stepsize
set_from_flat(thnew)
mr_loss_new, mr_losses_new = compute_mr_losses()
mr_impro = mr_losses_new - mr_lossbefore
meanlosses = surr, kl, *_ = allmean(np.array(mr_loss_new))
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 > adj_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!")
impro_save.append(np.hstack((mr_impro[:, 0], improve)))
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"):
#print('======================================= tdlamret ====================================')
#print(seg["tdlamret"])
for _ in range(vf_iters):
for (mbob, mbret) in dataset.iterbatches(
(seg["ob"], seg["tdlamret"]),
include_final_partial_batch=False,
batch_size=64):
#with tf.Session() as sess:
# sess.run(tf.global_variables_initializer())
# aaa = sess.run(pi.vf,feed_dict={ob:mbob,ret:mbret})
# print("aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa")
# print(aaa.shape)
# print(mbret.shape)
g = allmean(compute_vflossandgrad(mbob, mbret))
vfadam.update(g, vf_stepsize)
#print(mbob,mbret)
except:
print('error')
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()
#pdb.set_trace()
np.save(save_dir + 'coe.npy', coe_save)
np.save(save_dir + 'grad.npy', grad_save)
np.save(save_dir + 'improve.npy', impro_save)
np.save(save_dir + 'adj.npy', adj_save)
return pi
def learn(env, last_ob, last_jpos, run_reach, 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
):
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_grasp", ob_space, ac_space, reuse=(pretrained_weight != None))
oldpi = policy_func("oldpi", ob_space, ac_space)
atarg = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None]) # Target advantage function (if applicable)
ret = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
ob = U.get_placeholder_cached(name="ob")
ac = pi.pdtype.sample_placeholder([None])
# Changes are made in order to use tensorboard
# -------------------------------------------
#train_writer = tf.compat.v1.summary.FileWriter('../../logs/trpo_mpi') # sets log dir to GailPart folder
#sess = tf.compat.v1.Session() # create a session??
# -------------------------------------------
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_grasp/pol") or v.name.startswith("pi_grasp/logstd")]
vf_var_list = [v for v in all_var_list if v.name.startswith("pi_grasp/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.compat.v1.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.compat.v1.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, last_ob, last_jpos, run_reach, policy_func, 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"])
# 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:
t_name = task_name + "_" + str(iters_so_far)
fname = os.path.join(ckpt_dir, t_name) # changed from task_name
os.makedirs(os.path.dirname(fname), exist_ok=True)
saver = tf.compat.v1.train.Saver()
saver.save(tf.compat.v1.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__()
#print("trpo_mpi, seg = seg_gen.__next__() call output: ", seg )
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)))
#logger.log("trpo_mpi.py, what should be logged with loss names ie. meanlosses:_", meanlosses)
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
#logger.log("trpo_mpi.py, mean losses before logging wiht loss names: \n")
#logger.log(meanlosses)
# This is where the nan values are tabulated for some of the entries
#logger.log("trpo_mpi.py, view whats being printed with (loss_names, lossvalues)")
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)
# This is to see what the d_losses are
#logger.log("trpo_mpi.py, see what is being logged in d_losses")
#logger.log("trpo_mpi.py, d_losses")
#logger.log(d_losses)
logger.log(fmt_row(13, np.mean(d_losses, axis=0)))
# For Tensorboard Logging
# ---------------------------
#tf.compat.v1.summary.scalar("Generator Accuracy", tf.convert_to_tensor( np.mean(d_losses, axis=0)[4] ) ) # 5 position
#tf.compat.v1.summary.scalar("Expert Accuracy", tf.convert_to_tensor( np.mean(d_losses, axis=0)[5] ) ) # 6 position
#tf.compat.v1.summary.scalar("Entropy Loss", tf.convert_to_tensor( np.mean(d_losses, axis=0)[3] ) ) # 4 position
#merge = tf.compat.v1.summary.merge_all() # merge summaries
#summary = sess.run([merge])
#train_writer.add_summary(summary, iters_so_far)
# Is there a need to reset metric after every epoch? I dont think so?
# ---------------------------
#logger.log("trpo_mpi.py, after logging, but before recordeing timesteps so far")
lrlocal = (seg["ep_lens"], seg["ep_rets"], seg["ep_true_rets"]) # local values, truly confirmed is empty after call
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)
# Could it be that the seg locals for lens and rets are ommitted since has no use in gail algorithm?
# Probably dont have to worry about it, check the scalar part
logger.record_tabular("EpLenMean", np.mean(lenbuffer)) # This has nan values
logger.record_tabular("EpRewMean", np.mean(rewbuffer)) # This has nan values
logger.record_tabular("EpTrueRewMean", np.mean(true_rewbuffer)) # This has nan values
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
#timesteps_so_far += sum(lens)
timesteps_so_far += seg["steps"] # changed to match setup with no finishing condition
iters_so_far += 1
#env.reset() #reset the environment after a new iteration, therefore in traj generator check ob
logger.record_tabular("EpisodesSoFar", episodes_so_far) # This is 0 ? if lens which is the number of entries for episode length doesnt exist, doesnt make sense for it to have a return.
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
# I think the entloss, entrpoy, ev_.... and the useful ones arent from the environment called using the trpo
if rank == 0:
logger.dump_tabular()
def learn(
*,
network,
env,
eval_env,
make_eval_env,
env_id,
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,
# MBL
# For train mbl
mbl_train_freq=5,
# For eval
num_eval_episodes=5,
eval_freq=5,
vis_eval=False,
eval_targs=('mbmf', ),
#eval_targs=('mf',),
quant=2,
# For mbl.step
#num_samples=(1500,),
num_samples=(1, ),
horizon=(2, ),
#horizon=(2,1),
#num_elites=(10,),
num_elites=(1, ),
mbl_lamb=(1.0, ),
mbl_gamma=0.99,
#mbl_sh=1, # Number of step for stochastic sampling
mbl_sh=10000,
#vf_lookahead=-1,
#use_max_vf=False,
reset_per_step=(0, ),
# For get_model
num_fc=2,
num_fwd_hidden=500,
use_layer_norm=False,
# For MBL
num_warm_start=int(1e4),
init_epochs=10,
update_epochs=5,
batch_size=512,
update_with_validation=False,
use_mean_elites=1,
use_ent_adjust=0,
adj_std_scale=0.5,
# For data loading
validation_set_path=None,
# For data collect
collect_val_data=False,
# For traj collect
traj_collect='mf',
# For profile
measure_time=True,
eval_val_err=False,
measure_rew=True,
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):
'''
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 not isinstance(num_samples, tuple): num_samples = (num_samples, )
if not isinstance(horizon, tuple): horizon = (horizon, )
if not isinstance(num_elites, tuple): num_elites = (num_elites, )
if not isinstance(mbl_lamb, tuple): mbl_lamb = (mbl_lamb, )
if not isinstance(reset_per_step, tuple):
reset_per_step = (reset_per_step, )
if validation_set_path is None:
if collect_val_data:
validation_set_path = os.path.join(logger.get_dir(), 'val.pkl')
else:
validation_set_path = os.path.join('dataset',
'{}-val.pkl'.format(env_id))
if eval_val_err:
eval_val_err_path = os.path.join('dataset',
'{}-combine-val.pkl'.format(env_id))
logger.log(locals())
logger.log('MBL_SH', mbl_sh)
logger.log('Traj_collect', traj_collect)
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))
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)
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"):
pi = policy(observ_placeholder=ob)
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()
with tf.variable_scope("oldpi"):
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()
# MBL
# ---------------------------------------
#viz = Visdom(env=env_id)
win = None
eval_targs = list(eval_targs)
logger.log(eval_targs)
make_model_f = get_make_mlp_model(num_fc=num_fc,
num_fwd_hidden=num_fwd_hidden,
layer_norm=use_layer_norm)
mbl = MBL(env=eval_env,
env_id=env_id,
make_model=make_model_f,
num_warm_start=num_warm_start,
init_epochs=init_epochs,
update_epochs=update_epochs,
batch_size=batch_size,
**network_kwargs)
val_dataset = {'ob': None, 'ac': None, 'ob_next': None}
if update_with_validation:
logger.log('Update with validation')
val_dataset = load_val_data(validation_set_path)
if eval_val_err:
logger.log('Log val error')
eval_val_dataset = load_val_data(eval_val_err_path)
if collect_val_data:
logger.log('Collect validation data')
val_dataset_collect = []
def _mf_pi(ob, t=None):
stochastic = True
ac, vpred, _, _ = pi.step(ob, stochastic=stochastic)
return ac, vpred
def _mf_det_pi(ob, t=None):
#ac, vpred, _, _ = pi.step(ob, stochastic=False)
ac, vpred = pi._evaluate([pi.pd.mode(), pi.vf], ob)
return ac, vpred
def _mf_ent_pi(ob, t=None):
mean, std, vpred = pi._evaluate([pi.pd.mode(), pi.pd.std, pi.vf], ob)
ac = np.random.normal(mean, std * adj_std_scale, size=mean.shape)
return ac, vpred
################### use_ent_adjust======> adj_std_scale????????pi action sample
def _mbmf_inner_pi(ob, t=0):
if use_ent_adjust:
return _mf_ent_pi(ob)
else:
#return _mf_pi(ob)
if t < mbl_sh: return _mf_pi(ob)
else: return _mf_det_pi(ob)
# ---------------------------------------
# Run multiple configuration once
all_eval_descs = []
def make_mbmf_pi(n, h, e, l):
def _mbmf_pi(ob):
ac, rew = mbl.step(ob=ob,
pi=_mbmf_inner_pi,
horizon=h,
num_samples=n,
num_elites=e,
gamma=mbl_gamma,
lamb=l,
use_mean_elites=use_mean_elites)
return ac[None], rew
return Policy(step=_mbmf_pi, reset=None)
for n in num_samples:
for h in horizon:
for l in mbl_lamb:
for e in num_elites:
if 'mbmf' in eval_targs:
all_eval_descs.append(('MeanRew', 'MBL_TRPO_SIL',
make_mbmf_pi(n, h, e, l)))
#if 'mbmf' in eval_targs: all_eval_descs.append(('MeanRew-n-{}-h-{}-e-{}-l-{}-sh-{}-me-{}'.format(n, h, e, l, mbl_sh, use_mean_elites), 'MBL_TRPO-n-{}-h-{}-e-{}-l-{}-sh-{}-me-{}'.format(n, h, e, l, mbl_sh, use_mean_elites), make_mbmf_pi(n, h, e, l)))
if 'mf' in eval_targs:
all_eval_descs.append(
('MeanRew', 'TRPO_SIL', Policy(step=_mf_pi, reset=None)))
logger.log('List of evaluation targets')
for it in all_eval_descs:
logger.log(it[0])
pool = Pool(mp.cpu_count())
warm_start_done = False
# ----------------------------------------
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)
out = np.empty_like(x)
MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
out /= nworkers
return out
U.initialize()
if load_path is not None:
pi.load(load_path)
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
# ----------------------------------------
if traj_collect == 'mf':
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__()
if traj_collect == 'mf-random' or traj_collect == 'mf-mb':
seg_mbl = seg_gen_mbl.__next__()
else:
seg_mbl = seg
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"]
# Val data collection
if collect_val_data:
for ob_, ac_, ob_next_ in zip(ob[:-1, 0, ...], ac[:-1, ...],
ob[1:, 0, ...]):
val_dataset_collect.append(
(copy.copy(ob_), copy.copy(ac_), copy.copy(ob_next_)))
# -----------------------------
# MBL update
else:
ob_mbl, ac_mbl = seg_mbl["ob"], seg_mbl["ac"]
mbl.add_data_batch(ob_mbl[:-1, 0, ...], ac_mbl[:-1, ...],
ob_mbl[1:, 0, ...])
mbl.update_forward_dynamic(require_update=iters_so_far %
mbl_train_freq == 0,
ob_val=val_dataset['ob'],
ac_val=val_dataset['ac'],
ob_next_val=val_dataset['ob_next'])
# -----------------------------
if traj_collect == 'mf':
#if traj_collect == 'mf' or traj_collect == 'mf-random' or traj_collect == 'mf-mb':
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("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 sil_update > 0:
logger.record_tabular("SilSamples", sil_samples)
if rank == 0:
# MBL evaluation
if not collect_val_data:
#set_global_seeds(seed)
default_sess = tf.get_default_session()
def multithread_eval_policy(env_, pi_, num_episodes_,
vis_eval_, seed):
with default_sess.as_default():
if hasattr(env, 'ob_rms') and hasattr(env_, 'ob_rms'):
env_.ob_rms = env.ob_rms
res = eval_policy(env_, pi_, num_episodes_, vis_eval_,
seed, measure_time, measure_rew)
try:
env_.close()
except:
pass
return res
if mbl.is_warm_start_done() and iters_so_far % eval_freq == 0:
warm_start_done = mbl.is_warm_start_done()
if num_eval_episodes > 0:
targs_names = {}
with timed('eval'):
num_descs = len(all_eval_descs)
list_field_names = [e[0] for e in all_eval_descs]
list_legend_names = [e[1] for e in all_eval_descs]
list_pis = [e[2] for e in all_eval_descs]
list_eval_envs = [
make_eval_env() for _ in range(num_descs)
]
list_seed = [seed for _ in range(num_descs)]
list_num_eval_episodes = [
num_eval_episodes for _ in range(num_descs)
]
print(list_field_names)
print(list_legend_names)
list_vis_eval = [
vis_eval for _ in range(num_descs)
]
for i in range(num_descs):
field_name, legend_name = list_field_names[
i], list_legend_names[i],
res = multithread_eval_policy(
list_eval_envs[i], list_pis[i],
list_num_eval_episodes[i],
list_vis_eval[i], seed)
#eval_results = pool.starmap(multithread_eval_policy, zip(list_eval_envs, list_pis, list_num_eval_episodes, list_vis_eval,list_seed))
#for field_name, legend_name, res in zip(list_field_names, list_legend_names, eval_results):
perf, elapsed_time, eval_rew = res
logger.record_tabular(field_name, perf)
if measure_time:
logger.record_tabular(
'Time-%s' % (field_name), elapsed_time)
if measure_rew:
logger.record_tabular(
'SimRew-%s' % (field_name), eval_rew)
targs_names[field_name] = legend_name
if eval_val_err:
fwd_dynamics_err = mbl.eval_forward_dynamic(
obs=eval_val_dataset['ob'],
acs=eval_val_dataset['ac'],
obs_next=eval_val_dataset['ob_next'])
logger.record_tabular('FwdValError', fwd_dynamics_err)
logger.dump_tabular()
#print(logger.get_dir())
#print(targs_names)
#if num_eval_episodes > 0:
# win = plot(viz, win, logger.get_dir(), targs_names=targs_names, quant=quant, opt='best')
# -----------
#logger.dump_tabular()
yield pi
if collect_val_data:
with open(validation_set_path, 'wb') as f:
pickle.dump(val_dataset_collect, f)
logger.log('Save {} validation data'.format(len(val_dataset_collect)))
def learn(env,
policy_func,
reward_giver,
expert_dataset,
rank,
pretrained,
pretrained_weight,
*,
g_step,
d_step,
entcoeff,
ckpt_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,
rnd_iter=200,
callback=None,
dyn_norm=False,
mmd=False):
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)
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
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")]
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 = pi.vlossandgrad
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()
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
ep_stats = stats(["True_rewards", "Rewards", "Episode_length"])
# if provide pretrained weight
if pretrained_weight is not None:
U.load_variables(pretrained_weight, variables=pi.get_variables())
else:
if not dyn_norm:
pi.ob_rms.update(expert_dataset[0])
if not mmd:
reward_giver.train(*expert_dataset, iter=rnd_iter)
#inspect the reward learned
# for batch in iterbatches(expert_dataset, batch_size=32):
# print(reward_giver.get_reward(*batch))
best = -2000
save_ind = 0
max_save = 3
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)
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):
seg = seg_gen.__next__()
#mmd reward
if mmd:
reward_giver.set_b2(seg["ob"], seg["ac"])
seg["rew"] = reward_giver.get_reward(seg["ob"], seg["ac"])
#report stats and save policy if any good
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)
true_rew_avg = np.mean(true_rewbuffer)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpTrueRewMean", true_rew_avg)
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("Best so far", best)
# Save model
if ckpt_dir is not None and true_rew_avg >= best:
best = true_rew_avg
fname = os.path.join(ckpt_dir, task_name)
os.makedirs(os.path.dirname(fname), exist_ok=True)
pi.save_policy(fname + "_" + str(save_ind))
save_ind = (save_ind + 1) % max_save
#compute gradient towards next policy
add_vtarg_and_adv(seg, gamma, lam)
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") and dyn_norm:
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
*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:
stepdir = cg(fisher_vector_product,
g,
cg_iters=cg_iters,
verbose=False)
assert np.isfinite(stepdir).all()
shs = .5 * stepdir.dot(fisher_vector_product(stepdir))
lm = np.sqrt(shs / max_kl)
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:])
if pi.use_popart:
pi.update_popart(tdlamret)
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") and dyn_norm:
pi.ob_rms.update(
mbob) # update running mean/std for policy
vfadam.update(allmean(compute_vflossandgrad(mbob, mbret)),
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))
if rank == 0:
logger.dump_tabular()
class TRPO_agent_new(object):
index2 = 0
def __init__(self, a_name, env, policy_func, par):
self.env = env
self.timesteps_per_batch = par.timesteps_per_batch
self.max_kl = par.max_kl
self.cg_iters = par.cg_iters
self.gamma = par.gamma
self.lam = par.lam # advantage estimation
self.entcoeff = par.entcoeff
self.cg_damping = par.cg_damping
self.vf_stepsize = par.vf_stepsize
self.vf_iters = par.vf_iters
self.max_timesteps = par.max_timesteps
self.max_episodes = par.max_episodes
self.max_iters = par.max_iters
self.callback = par.callback, # you can do anything in the callback, since it takes locals(), globals()
self.nworkers = MPI.COMM_WORLD.Get_size()
self.rank = MPI.COMM_WORLD.Get_rank()
np.set_printoptions(precision=3)
# Setup losses and stuff
# ----------------------------------------
self.ob_space = self.env.observation_space
self.ac_space = self.env.action_space
self.pi = policy_func(a_name, self.ob_space, self.ac_space)
self.oldpi = policy_func("oldpi" + a_name, self.ob_space,
self.ac_space)
self.atarg = tf.placeholder(
dtype=tf.float32,
shape=[None]) # Target advantage function (if applicable)
self.ret = tf.placeholder(dtype=tf.float32,
shape=[None]) # Empirical return
self.ob = U.get_placeholder_cached(name="ob" +
str(TRPO_agent_new.index2))
self.ac = self.pi.pdtype.sample_placeholder([None])
self.kloldnew = self.oldpi.pd.kl(self.pi.pd)
self.ent = self.pi.pd.entropy()
meankl = U.mean(self.kloldnew)
meanent = U.mean(self.ent)
entbonus = self.entcoeff * meanent
self.vferr = U.mean(tf.square(self.pi.vpred - self.ret))
ratio = tf.exp(self.pi.pd.logp(self.ac) -
self.oldpi.pd.logp(self.ac)) # advantage * pnew / pold
surrgain = U.mean(ratio * self.atarg)
optimgain = surrgain + entbonus
self.losses = [optimgain, meankl, entbonus, surrgain, meanent]
self.loss_names = [
"optimgain", "meankl", "entloss", "surrgain", "entropy"
]
self.dist = meankl
all_var_list = self.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")
]
self.vfadam = MpiAdam(vf_var_list)
self.get_flat = U.GetFlat(var_list)
self.set_from_flat = U.SetFromFlat(var_list)
self.klgrads = tf.gradients(self.dist, var_list)
self.flat_tangent = tf.placeholder(dtype=tf.float32,
shape=[None],
name="flat_tan" +
str(TRPO_agent_new.index2))
shapes = [var.get_shape().as_list() for var in var_list]
start = 0
self.tangents = []
for shape in shapes:
sz = U.intprod(shape)
self.tangents.append(
tf.reshape(self.flat_tangent[start:start + sz], shape))
start += sz
self.gvp = tf.add_n([
U.sum(g * tangent)
for (g, tangent) in zipsame(self.klgrads, self.tangents)
]) #pylint: disable=E1111
self.fvp = U.flatgrad(self.gvp, var_list)
self.assign_old_eq_new = U.function(
[], [],
updates=[
tf.assign(oldv, newv) for (oldv, newv) in zipsame(
self.oldpi.get_variables(), self.pi.get_variables())
])
self.compute_losses = U.function([self.ob, self.ac, self.atarg],
self.losses)
self.compute_lossandgrad = U.function(
[self.ob, self.ac, self.atarg],
self.losses + [U.flatgrad(optimgain, var_list)])
self.compute_fvp = U.function(
[self.flat_tangent, self.ob, self.ac, self.atarg], self.fvp)
self.compute_vflossandgrad = U.function([self.ob, self.ret],
U.flatgrad(
self.vferr, vf_var_list))
TRPO_agent_new.index2 += 1
U.initialize()
self.th_init = self.get_flat()
MPI.COMM_WORLD.Bcast(self.th_init, root=0)
self.set_from_flat(self.th_init)
self.vfadam.sync()
print("Init param sum", self.th_init.sum(), flush=True)
@contextmanager
def timed(self, msg):
if self.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(self, x):
assert isinstance(x, np.ndarray)
out = np.empty_like(x)
MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
out /= self.nworkers ####################################
return out
def fisher_vector_product(self, p):
return self.allmean(self.compute_fvp(
p, *self.fvpargs)) + self.cg_damping * p
def learn(self):
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(self.pi,
self.env,
self.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([
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
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 action_ev(self, obs):
ac, vpred = self.pi.act(False, obs)
return ac
def restore(self, folder):
U.load_state(folder + '/data')
def save_data(self, folder):
try:
os.makedirs(folder)
except OSError as e:
if e.errno != errno.EEXIST:
raise
saver = U.tf.train.Saver()
saver.save(U.get_session(), folder + '/data')
def learn(env,
policy_func,
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=1e-4,
vf_iters=3,
max_timesteps=0,
max_episodes=0,
max_iters=0,
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,
) # 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")
ob_config = U.get_placeholder_cached(name="ob")
ob_target = U.get_placeholder_cached(name="goal")
obs_pos = U.get_placeholder_cached(name="obs_pos")
#obs_pos2 = U.get_placeholder_cached(name="obs_pos2")
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") or v.name.startswith("pi/obs")
]
vf_var_list = [
v for v in all_var_list
if v.name.startswith("pi/vf") or v.name.startswith("pi/obs")
]
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_config, ob_target, obs_pos, ac, atarg],
losses)
compute_lossandgrad = U.function(
[ob_config, ob_target, obs_pos, ac, atarg],
losses + [U.flatgrad(optimgain, var_list)])
compute_fvp = U.function(
[flat_tangent, ob_config, ob_target, obs_pos, ac, atarg], fvp)
compute_vflossandgrad = U.function([ob_config, ob_target, obs_pos, 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()
if rank == 0:
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
true_rewbuffer = deque(maxlen=40)
max_trm = -5
true_reward_mean = 0
assert sum([max_iters > 0, max_timesteps > 0, max_episodes > 0]) == 1
g_loss_stats = stats(loss_names)
ep_stats = stats(["True_rewards", "Rewards", "Episode_length"])
# if provide pretrained weight
if pretrained_weight is not None:
#U.load_variables(pretrained_weight, variables=pi.get_variables())
saver = tf.train.Saver()
saver.restore(tf.get_default_session(), pretrained_weight)
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 ckpt_dir is not None and true_reward_mean > max_trm:
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)
max_trm = true_reward_mean
logger.log("********** Iteration %i ************" % iters_so_far)
def fisher_vector_product(p):
v1 = allmean(compute_fvp(p, *fvpargs))
# print("norm(v1):%.2e, norm(p):%.2e, cg_damping:%.2e"%(np.linalg.norm(v1), np.linalg.norm(p), cg_damping))
return v1 + 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
config, goal, obstacle_pos = [], [], []
for o in seg["ob"]:
config.append(o["joint"])
goal.append(o["target"])
obstacle_pos.append(o["obstacle_pos1"])
#obstacle_pos2.append(o["obstacle_pos2"])
config, goal, obstacle_pos = map(np.array,
[config, goal, obstacle_pos])
args = config, goal, obstacle_pos, 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)
logger.log(
'iter:{:d}, norm of g: {:.4f}, error of cg: {:.4f}'.
format(
cg_iters, np.linalg.norm(g),
np.linalg.norm(g -
compute_fvp(stepdir, *fvpargs))))
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, mbg, mbop, mbret) in dataset.iterbatches(
(config, goal, obstacle_pos, 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, mbg, mbop, 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))
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))
true_reward_mean = 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()
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
class RLTrainer(object):
def __init__(self, env, policy, old_policy, config):
self._env = env
self._config = config
self.policy = policy
self.old_policy = old_policy
self._entcoeff = config.entcoeff
self._optim_epochs = config.optim_epochs
self._optim_stepsize = config.optim_stepsize
self._optim_batchsize = config.optim_batchsize
# global step
self.global_step = tf.Variable(0,
name='global_step',
dtype=tf.int64,
trainable=False)
self.update_global_step = tf.assign(self.global_step,
self.global_step + 1)
# tensorboard summary
self._is_chef = (MPI.COMM_WORLD.Get_rank() == 0)
self._num_workers = MPI.COMM_WORLD.Get_size()
if self._is_chef:
self.summary_name = ["reward", "length"]
self.summary_name += env.unwrapped.reward_type
# build loss/optimizers
if self._config.rl_method == 'trpo':
self._build_trpo()
elif self._config.rl_method == 'ppo':
self._build_ppo()
else:
raise NotImplementedError
if self._is_chef and self._config.is_train:
self.ep_stats = stats(self.summary_name)
self.writer = U.file_writer(config.log_dir)
@contextmanager
def timed(self, msg):
if self._is_chef:
logger.info(colorize(msg, color='magenta'))
tstart = time.time()
yield
logger.info(
colorize("done in %.3f seconds" % (time.time() - tstart),
color='magenta'))
else:
yield
def _all_mean(self, x):
assert isinstance(x, np.ndarray)
out = np.empty_like(x)
MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
out /= self._num_workers
return out
def _build_ppo(self):
config = self._config
pi = self.policy
oldpi = self.old_policy
# input placeholders
obs = pi.obs
ac = pi.pdtype.sample_placeholder([None], name='action')
atarg = tf.placeholder(dtype=tf.float32,
shape=[None],
name='advantage')
ret = tf.placeholder(dtype=tf.float32, shape=[None], name='return')
lrmult = tf.placeholder(name='lrmult', dtype=tf.float32, shape=[])
self._clip_param = config.clip_param * lrmult
# policy
var_list = pi.get_trainable_variables()
self._adam = MpiAdam(var_list)
fetch_dict = self.policy_loss_ppo(pi, oldpi, ac, atarg, ret)
if self._is_chef:
self.summary_name += ['ppo/' + key for key in fetch_dict.keys()]
self.summary_name += ['ppo/grad_norm', 'ppo/grad_norm_clipped']
fetch_dict['g'] = U.flatgrad(fetch_dict['total_loss'], var_list)
self._loss = U.function([lrmult] + obs + [ac, atarg, ret], fetch_dict)
self._update_oldpi = U.function(
[], [],
updates=[
tf.assign(oldv, newv) for (
oldv,
newv) in zipsame(oldpi.get_variables(), pi.get_variables())
])
# initialize and sync
U.initialize()
self._adam.sync()
def _build_trpo(self):
pi = self.policy
oldpi = self.old_policy
# input placeholders
obs = pi.obs
ac = pi.pdtype.sample_placeholder([None], name='action')
atarg = tf.placeholder(
dtype=tf.float32, shape=[None],
name='advantage') # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None],
name='return') # Empirical return
# policy
all_var_list = pi.get_trainable_variables()
self.pol_var_list = [
v for v in all_var_list if v.name.split("/")[2].startswith("pol")
]
self.vf_var_list = [
v for v in all_var_list if v.name.split("/")[2].startswith("vf")
]
self._vf_adam = MpiAdam(self.vf_var_list)
kl_oldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
mean_kl = tf.reduce_mean(kl_oldnew)
mean_ent = tf.reduce_mean(ent)
pol_entpen = -self._config.entcoeff * mean_ent
vf_loss = tf.reduce_mean(tf.square(pi.vpred - ret))
ratio = tf.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac))
pol_surr = tf.reduce_mean(ratio * atarg)
pol_loss = pol_surr + pol_entpen
pol_losses = {
'pol_loss': pol_loss,
'pol_surr': pol_surr,
'pol_entpen': pol_entpen,
'kl': mean_kl,
'entropy': mean_ent
}
if self._is_chef:
self.summary_name += ['trpo/vf_loss']
self.summary_name += ['trpo/' + key for key in pol_losses.keys()]
self._get_flat = U.GetFlat(self.pol_var_list)
self._set_from_flat = U.SetFromFlat(self.pol_var_list)
klgrads = tf.gradients(mean_kl, self.pol_var_list)
flat_tangent = tf.placeholder(dtype=tf.float32,
shape=[None],
name="flat_tan")
shapes = [var.get_shape().as_list() for var in self.pol_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, self.pol_var_list)
self._update_oldpi = U.function(
[], [],
updates=[
tf.assign(oldv, newv) for (
oldv,
newv) in zipsame(oldpi.get_variables(), pi.get_variables())
])
self._compute_losses = U.function(obs + [ac, atarg], pol_losses)
pol_losses = dict(pol_losses)
pol_losses.update({'g': U.flatgrad(pol_loss, self.pol_var_list)})
self._compute_lossandgrad = U.function(obs + [ac, atarg], pol_losses)
self._compute_fvp = U.function([flat_tangent] + obs + [ac, atarg], fvp)
self._compute_vflossandgrad = U.function(
obs + [ret], U.flatgrad(vf_loss, self.vf_var_list))
self._compute_vfloss = U.function(obs + [ret], vf_loss)
# initialize and sync
U.initialize()
th_init = self._get_flat()
MPI.COMM_WORLD.Bcast(th_init, root=0)
self._set_from_flat(th_init)
self._vf_adam.sync()
logger.info("Init param sum", th_init.sum())
def policy_loss_ppo(self, pi, oldpi, ac, atarg, ret):
kl_oldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
mean_kl = U.mean(kl_oldnew)
mean_ent = U.mean(ent)
pol_entpen = -self._entcoeff * mean_ent
action_prob = pi.pd.logp(ac) - oldpi.pd.logp(ac)
action_loss = tf.exp(action_prob) * atarg
ratio = tf.exp(action_prob)
surr1 = ratio * atarg
surr2 = U.clip(ratio, 1.0 - self._clip_param,
1.0 + self._clip_param) * atarg
pol_surr = -U.mean(tf.minimum(surr1, surr2))
vf_loss = U.mean(tf.square(pi.vpred - ret))
total_loss = pol_surr + pol_entpen + vf_loss
losses = {
'total_loss': total_loss,
'action_loss': action_loss,
'pol_surr': pol_surr,
'pol_entpen': pol_entpen,
'kl': mean_kl,
'entropy': mean_ent,
'vf_loss': vf_loss
}
return losses
def _summary(self, it):
if self._is_chef:
if it % self._config.ckpt_save_step == 0:
fname = osp.join(self._config.log_dir, '%.5d' % it)
U.save_state(fname)
def train(self, rollout):
config = self._config
sess = U.get_session()
global_step = sess.run(self.global_step)
t = trange(global_step,
config.max_iters,
total=config.max_iters,
initial=global_step)
info = None
for step in t:
# backup checkpoint
self._summary(step)
self._cur_lrmult = max(1.0 - float(step) / config.max_iters, 0)
# rollout
with self.timed("sampling"):
rolls = rollout.__next__()
if config.rl_method == 'trpo':
rollouts.add_advantage_rl(rolls, 0.99, 0.98)
elif config.rl_method == 'ppo':
rollouts.add_advantage_rl(rolls, 0.99, 0.95)
# train policy
info = self._update_policy(rolls, step)
if self._is_chef:
ep = len(rolls["ep_length"])
reward_mean = np.mean(rolls["ep_reward"])
reward_std = np.std(rolls["ep_reward"])
length_mean = np.mean(rolls["ep_length"])
length_std = np.std(rolls["ep_length"])
desc = "ep(%d) reward(%.1f, %.1f) length(%d, %.1f)" % (
ep, reward_mean, reward_std, length_mean, length_std)
for key, value in rolls.items():
if key.startswith('ep_'):
info[key.split('ep_')[1]] = np.mean(value)
# log
if self._is_chef:
self.ep_stats.add_all_summary_dict(self.writer, info,
global_step)
t.set_description(desc)
global_step = sess.run(self.update_global_step)
def evaluate(self, rollout, ckpt_num=None):
config = self._config
ep_lens = []
ep_rets = []
ep_success = []
if config.record:
record_dir = osp.join(config.log_dir, 'video')
os.makedirs(record_dir, exist_ok=True)
if config.is_collect_state:
state_dir = osp.join(config.log_dir, 'state')
os.makedirs(state_dir, exist_ok=True)
state_file = h5py.File(
osp.join(
state_dir,
'seed_{}_traj_{}.hdf5'.format(config.seed,
config.num_evaluation_run)),
'w')
for _ in range(config.num_evaluation_run):
ep_traj = rollout.__next__()
ep_lens.append(ep_traj["ep_length"][0])
ep_rets.append(ep_traj["ep_reward"][0])
if "ep_success" not in ep_traj:
ep_success.append(0)
else:
ep_success.append(np.sum(ep_traj["ep_success"]))
if config.evaluation_log:
logger.log('Trial #{}: lengths {}, returns {}'.format(
_, ep_traj["ep_length"][0], ep_traj["ep_reward"][0]))
# Video recording
if config.record:
visual_obs = ep_traj["visual_obs"]
video_name = (config.video_prefix
or '') + '{}{}_rew_{:.2f}_len_{}.mp4'.format(
'' if ckpt_num is None else
'ckpt_{}_'.format(ckpt_num), _,
ep_traj["ep_reward"][0],
ep_traj["ep_length"][0])
video_path = osp.join(record_dir, video_name)
fps = 60.
def f(t):
frame_length = len(visual_obs)
new_fps = 1. / (1. / fps + 1. / frame_length)
idx = min(int(t * new_fps), frame_length - 1)
return visual_obs[idx]
video = mpy.VideoClip(f, duration=len(visual_obs) / fps + 2)
video.write_videofile(video_path,
fps,
verbose=False,
progress_bar=False)
if config.is_collect_state:
grp = state_file.create_group('traj_{}'.format(_))
grp["obs"] = ep_traj["obs"]
grp["len"] = ep_traj["ep_length"][0]
grp["ret"] = ep_traj["ep_reward"][0]
try:
grp["success"] = ep_traj["ep_success"][0]
except:
pass
if config.is_collect_state:
state_file.close()
logger.log('Episode Length: {}'.format(
sum(ep_lens) / config.num_evaluation_run))
logger.log('Episode Rewards: {}'.format(
sum(ep_rets) / config.num_evaluation_run))
logger.log('Episode Success: {}'.format(
sum(ep_success) / config.num_evaluation_run))
if config.final_eval:
path = os.path.join(os.path.expanduser('~'), 'iclr_eval',
self._config.prefix + ".txt")
with open(path, "w") as f:
for s in ep_success:
f.write(str(s))
f.write('\n')
def _update_policy(self, seg, it):
if self._config.rl_method == 'trpo':
info = self._update_policy_trpo(seg, it)
elif self._config.rl_method == 'ppo':
info = self._update_policy_ppo(seg)
return info
def _update_policy_trpo(self, seg, it):
pi = self.policy
ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg[
"tdlamret"]
atarg = (atarg - atarg.mean()) / atarg.std()
if self._is_chef:
info = defaultdict(list)
ob_dict = self._env.get_ob_dict(ob)
for ob_name in pi.ob_type:
pi.ob_rms[ob_name].update(ob_dict[ob_name])
ob_list = pi.get_ob_list(ob_dict)
args = ob_list + [ac, atarg]
fvpargs = [arr[::5] for arr in args]
def fisher_vector_product(p):
return self._all_mean(self._compute_fvp(
p, *fvpargs)) + self._config.cg_damping * p
self._update_oldpi()
with self.timed("computegrad"):
lossbefore = self._compute_lossandgrad(*args)
lossbefore = {
k: self._all_mean(np.array(lossbefore[k]))
for k in sorted(lossbefore.keys())
}
g = lossbefore['g']
if np.allclose(g, 0):
logger.log("Got zero gradient. not updating")
else:
with self.timed("cg"):
stepdir = cg(fisher_vector_product,
g,
cg_iters=self._config.cg_iters,
verbose=self._is_chef)
assert np.isfinite(stepdir).all()
shs = .5 * stepdir.dot(fisher_vector_product(stepdir))
lm = np.sqrt(shs / self._config.max_kl)
fullstep = stepdir / lm
expectedimprove = g.dot(fullstep)
surrbefore = lossbefore['pol_loss']
stepsize = 1.0
thbefore = self._get_flat()
for _ in range(10):
thnew = thbefore + fullstep * stepsize
self._set_from_flat(thnew)
meanlosses = self._compute_losses(*args)
meanlosses = {
k: self._all_mean(np.array(meanlosses[k]))
for k in sorted(meanlosses.keys())
}
# logger.info('mean', [float(meanlosses[k]) for k in ['pol_loss', 'kl', 'pol_entpen', 'pol_surr', 'entropy']])
if self._is_chef:
for key, value in meanlosses.items():
if key != 'g':
info['trpo/' + key].append(value)
surr = meanlosses['pol_loss']
kl = meanlosses['kl']
meanlosses = np.array(list(meanlosses.values()))
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._config.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._num_workers > 1 and it % 20 == 0:
paramsums = MPI.COMM_WORLD.allgather(
(thnew.sum(),
self._vf_adam.getflat().sum())) # list of tuples
assert all(
np.allclose(ps, paramsums[0]) for ps in paramsums[1:])
with self.timed("vf"):
for _ in range(self._config.vf_iters):
for (mbob, mbret) in dataset.iterbatches(
(ob, tdlamret),
include_final_partial_batch=False,
batch_size=64):
ob_list = pi.get_ob_list(mbob)
g = self._all_mean(
self._compute_vflossandgrad(*ob_list, mbret))
self._vf_adam.update(g, self._config.vf_stepsize)
vf_loss = self._all_mean(
np.array(self._compute_vfloss(*ob_list, mbret)))
if self._is_chef:
info['trpo/vf_loss'].append(vf_loss)
if self._is_chef:
for key, value in info.items():
info[key] = np.mean(value)
return info
return None
def _update_policy_ppo(self, seg):
pi = self.policy
ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg[
"tdlamret"]
atarg = (atarg - atarg.mean()) / max(atarg.std(), 0.000001)
if self._is_chef:
info = defaultdict(list)
optim_batchsize = min(self._optim_batchsize, ob.shape[0])
# prepare batches
d = dataset.Dataset(dict(ob=ob, ac=ac, atarg=atarg, vtarg=tdlamret),
shuffle=True)
ob_dict = self._env.get_ob_dict(ob)
for ob_name in pi.ob_type:
pi.ob_rms[ob_name].update(ob_dict[ob_name])
self._update_oldpi()
with self.timed("update"):
for _ in range(self._optim_epochs):
for batch in d.iterate_once(optim_batchsize):
ob_list = pi.get_ob_list(batch["ob"])
fetched = self._loss(self._cur_lrmult, *ob_list,
batch["ac"], batch["atarg"],
batch["vtarg"])
self._adam.update(fetched['g'],
self._optim_stepsize * self._cur_lrmult)
if self._is_chef:
for key, value in fetched.items():
if key != 'g':
if np.isscalar(value):
info['ppo/' + key].append(value)
else:
info['ppo/' + key].extend(value)
else:
grad_norm_value = np.linalg.norm(value)
info['ppo/grad_norm'].append(grad_norm_value)
info['ppo/grad_norm_clipped'].append(
np.clip(grad_norm_value, 0,
self._config.trans_max_grad_norm))
if self._is_chef:
for key, value in info.items():
info[key] = np.mean(value)
return info
return None
