def estimate_advantage_function(vf, paths, parameters):
advs = []
for path in paths:
rew_t = path["reward"]
print('len(rew_t): ', len(rew_t))
return_t = common.discount(rew_t, parameters.gamma)
vpred_t = vf.predict(path)
vpred_t = np.append(vpred_t,
0.0 if path["terminated"] else vpred_t[-1])
delta_t = rew_t + parameters.gamma * vpred_t[1:] - vpred_t[:-1]
adv_t = common.discount(delta_t, parameters.gamma * parameters.lam)
print('len(adv_t): ', len(adv_t))
advs.append(adv_t)
return advs
def estimate_v_targets(vf, paths, parameters):
vtargs = []
for path in paths:
rew_t = path["reward"]
return_t = common.discount(rew_t, parameters.gamma)
vtargs.append(return_t)
return vtargs
def learn(env,
policy,
value_fn,
gamma,
lam,
timesteps_per_batch,
num_timesteps,
animate=False,
callback=None,
desired_kl=0.002):
"""
Traines an ACKTR model.
:param env: (Gym environment) The environment to learn from
:param policy: (Object) The policy model to use (MLP, CNN, LSTM, ...)
:param value_fn: (Object) The value function model to use (MLP, CNN, LSTM, ...)
:param gamma: (float) The discount value
:param lam: (float) the tradeoff between exploration and exploitation
:param timesteps_per_batch: (int) the number of timesteps for each batch
:param num_timesteps: (int) the total number of timesteps to run
:param animate: (bool) if render env
:param callback: (function) called every step, used for logging and saving
:param desired_kl: (float) the Kullback leibler weight for the loss
"""
obfilter = ZFilter(env.observation_space.shape)
max_pathlength = env.spec.timestep_limit
stepsize = tf.Variable(initial_value=np.float32(np.array(0.03)),
name='stepsize')
inputs, loss, loss_sampled = policy.update_info
optim = kfac.KfacOptimizer(learning_rate=stepsize,
cold_lr=stepsize * (1 - 0.9),
momentum=0.9,
kfac_update=2,
epsilon=1e-2,
stats_decay=0.99,
async=1,
cold_iter=1,
weight_decay_dict=policy.wd_dict,
max_grad_norm=None)
pi_var_list = []
for var in tf.trainable_variables():
if "pi" in var.name:
pi_var_list.append(var)
update_op, q_runner = optim.minimize(loss,
loss_sampled,
var_list=pi_var_list)
do_update = tf_util.function(inputs, update_op)
tf_util.initialize()
# start queue runners
enqueue_threads = []
coord = tf.train.Coordinator()
for queue_runner in [q_runner, value_fn.q_runner]:
assert queue_runner is not None
enqueue_threads.extend(
queue_runner.create_threads(tf.get_default_session(),
coord=coord,
start=True))
i = 0
timesteps_so_far = 0
while True:
if timesteps_so_far > num_timesteps:
break
logger.log("********** Iteration %i ************" % i)
# Collect paths until we have enough timesteps
timesteps_this_batch = 0
paths = []
while True:
path = rollout(env,
policy,
max_pathlength,
animate=(len(paths) == 0 and (i % 10 == 0)
and animate),
obfilter=obfilter)
paths.append(path)
timesteps_this_batch += path["reward"].shape[0]
timesteps_so_far += path["reward"].shape[0]
if timesteps_this_batch > timesteps_per_batch:
break
# Estimate advantage function
vtargs = []
advs = []
for path in paths:
rew_t = path["reward"]
return_t = common.discount(rew_t, gamma)
vtargs.append(return_t)
vpred_t = value_fn.predict(path)
vpred_t = np.append(vpred_t,
0.0 if path["terminated"] else vpred_t[-1])
delta_t = rew_t + gamma * vpred_t[1:] - vpred_t[:-1]
adv_t = common.discount(delta_t, gamma * lam)
advs.append(adv_t)
# Update value function
value_fn.fit(paths, vtargs)
# Build arrays for policy update
ob_no = np.concatenate([path["observation"] for path in paths])
action_na = np.concatenate([path["action"] for path in paths])
oldac_dist = np.concatenate([path["action_dist"] for path in paths])
adv_n = np.concatenate(advs)
standardized_adv_n = (adv_n - adv_n.mean()) / (adv_n.std() + 1e-8)
# Policy update
do_update(ob_no, action_na, standardized_adv_n)
min_stepsize = np.float32(1e-8)
max_stepsize = np.float32(1e0)
# Adjust stepsize
kl_loss = policy.compute_kl(ob_no, oldac_dist)
if kl_loss > desired_kl * 2:
logger.log("kl too high")
tf.assign(stepsize, tf.maximum(min_stepsize,
stepsize / 1.5)).eval()
elif kl_loss < desired_kl / 2:
logger.log("kl too low")
tf.assign(stepsize, tf.minimum(max_stepsize,
stepsize * 1.5)).eval()
else:
logger.log("kl just right!")
logger.record_tabular(
"EpRewMean", np.mean([path["reward"].sum() for path in paths]))
logger.record_tabular(
"EpRewSEM",
np.std([
path["reward"].sum() / np.sqrt(len(paths)) for path in paths
]))
logger.record_tabular(
"EpLenMean", np.mean([path["reward"].shape[0] for path in paths]))
logger.record_tabular("KL", kl_loss)
if callback:
callback()
logger.dump_tabular()
i += 1
coord.request_stop()
coord.join(enqueue_threads)
def learn(env,
policy,
vf,
gamma,
lam,
timesteps_per_batch,
resume,
logdir,
agentName,
num_timesteps,
animate=False,
callback=None,
desired_kl=0.002):
obfilter = ZFilter(env.observation_space.shape)
max_pathlength = env.spec.timestep_limit
stepsize = tf.Variable(initial_value=np.float32(np.array(0.03)),
name='stepsize')
inputs, loss, loss_sampled = policy.update_info
optim = kfac.KfacOptimizer(learning_rate=stepsize, cold_lr=stepsize*(1.0 - 0.9), momentum=0.9, kfac_update=2,\
epsilon=1e-2, stats_decay=0.99, async=1, cold_iter=1,
weight_decay_dict=policy.wd_dict, max_grad_norm=None)
pi_var_list = []
for var in tf.trainable_variables():
if "pi" in var.name:
pi_var_list.append(var)
update_op, q_runner = optim.minimize(loss,
loss_sampled,
var_list=pi_var_list)
do_update = U.function(inputs, update_op)
U.initialize()
# start queue runners
enqueue_threads = []
coord = tf.train.Coordinator()
for qr in [q_runner, vf.q_runner]:
assert (qr != None)
enqueue_threads.extend(
qr.create_threads(U.get_session(), coord=coord, start=True))
timesteps_so_far = 0
saver = tf.train.Saver(max_to_keep=10)
if resume > 0:
saver.restore(
tf.get_default_session(),
os.path.join(os.path.abspath(logdir),
"{}-{}".format(agentName, resume)))
ob_filter_path = os.path.join(os.path.abspath(logdir),
"{}-{}".format('obfilter', resume))
with open(ob_filter_path, 'rb') as ob_filter_input:
obfilter = pickle.load(ob_filter_input)
print("Loaded observation filter")
iters_so_far = resume
print('logdir = ', logdir)
logF = open(os.path.join(logdir, 'log.txt'), 'a')
logF2 = open(os.path.join(logdir, 'log_it.txt'), 'a')
logStats = open(os.path.join(logdir, 'log_stats.txt'), 'a')
while True:
if timesteps_so_far > num_timesteps:
break
logger.log("********** Iteration %i ************" % iters_so_far)
save_interval = 5
# Collect paths until we have enough timesteps
timesteps_this_batch = 0
paths = []
while True:
path = rollout(env,
policy,
max_pathlength,
animate=(len(paths) == 0
and (iters_so_far % save_interval == 0)
and animate),
obfilter=obfilter)
paths.append(path)
n = pathlength(path)
timesteps_this_batch += n
timesteps_so_far += n
if timesteps_this_batch > timesteps_per_batch:
break
# Estimate advantage function
vtargs = []
advs = []
for path in paths:
rew_t = path["reward"]
return_t = common.discount(rew_t, gamma)
vtargs.append(return_t)
vpred_t = vf.predict(path)
vpred_t = np.append(vpred_t,
0.0 if path["terminated"] else vpred_t[-1])
delta_t = rew_t + gamma * vpred_t[1:] - vpred_t[:-1]
adv_t = common.discount(delta_t, gamma * lam)
advs.append(adv_t)
# Update value function
vf.fit(paths, vtargs)
# Build arrays for policy update
ob_no = np.concatenate([path["observation"] for path in paths])
action_na = np.concatenate([path["action"] for path in paths])
oldac_dist = np.concatenate([path["action_dist"] for path in paths])
adv_n = np.concatenate(advs)
standardized_adv_n = (adv_n - adv_n.mean()) / (adv_n.std() + 1e-8)
# Policy update
do_update(ob_no, action_na, standardized_adv_n)
min_stepsize = np.float32(1e-8)
max_stepsize = np.float32(1e0)
# Adjust stepsize
kl = policy.compute_kl(ob_no, oldac_dist)
if kl > desired_kl * 2.0:
logger.log("kl too high")
U.eval(
tf.assign(stepsize, tf.maximum(min_stepsize, stepsize / 1.5)))
elif kl < desired_kl / 2.0:
logger.log("kl too low")
U.eval(
tf.assign(stepsize, tf.minimum(max_stepsize, stepsize * 1.5)))
else:
logger.log("kl just right!")
rew_mean = np.mean([path["reward"].sum() for path in paths])
logger.record_tabular("EpRewMean", rew_mean)
logger.record_tabular(
"EpRewSEM",
np.std([
path["reward"].sum() / np.sqrt(len(paths)) for path in paths
]))
logger.record_tabular("EpLenMean",
np.mean([pathlength(path) for path in paths]))
logger.record_tabular("KL", kl)
logF.write(str(rew_mean) + "\n")
logF2.write(str(iters_so_far) + "," + str(rew_mean) + "\n")
# json.dump(combined_stats, logStats)
logF.flush()
logF2.flush()
# logStats.flush()
if save_interval and (iters_so_far % save_interval == 0
or iters_so_far == 1):
saver.save(tf.get_default_session(),
os.path.join(logdir, agentName),
global_step=iters_so_far)
ob_filter_path = os.path.join(
os.path.abspath(logdir),
"{}-{}".format('obfilter', iters_so_far))
with open(ob_filter_path, 'wb') as ob_filter_output:
pickle.dump(obfilter, ob_filter_output,
pickle.HIGHEST_PROTOCOL)
if callback:
callback()
logger.dump_tabular()
iters_so_far += 1
coord.request_stop()
coord.join(enqueue_threads)
def learn(env,
policy,
vf,
gamma,
lam,
timesteps_per_batch,
num_timesteps,
animate=False,
callback=None,
desired_kl=0.002,
fname=None):
obfilter = ZFilter(env.observation_space.shape)
max_pathlength = env.spec.timestep_limit
stepsize = tf.Variable(initial_value=np.float32(np.array(0.03)),
name='stepsize')
inputs, loss, loss_sampled = policy.update_info
optim = kfac.KfacOptimizer(learning_rate=stepsize, cold_lr=stepsize*(1-0.9), momentum=0.9, kfac_update=2,\
epsilon=1e-2, stats_decay=0.99, async=1, cold_iter=1,
weight_decay_dict=policy.wd_dict, max_grad_norm=None)
pi_var_list = []
for var in tf.trainable_variables():
if "pi" in var.name:
pi_var_list.append(var)
update_op, q_runner = optim.minimize(loss,
loss_sampled,
var_list=pi_var_list)
do_update = U.function(inputs, update_op)
U.initialize()
if fname != None and tf.train.checkpoint_exists(fname):
load_result = U.load_state(fname)
logger.log("Model loaded from file {}".format(fname))
# start queue runners
enqueue_threads = []
coord = tf.train.Coordinator()
for qr in [q_runner, vf.q_runner]:
assert (qr != None)
enqueue_threads.extend(
qr.create_threads(get_session(), coord=coord, start=True))
i = 0
timesteps_so_far = 0
while True:
if timesteps_so_far > num_timesteps:
break
logger.log("********** Iteration %i ************" % i)
# Save model every 100 iterations
if fname != None and (i % 100 == 99):
U.save_state(fname)
logger.log("Model saved to file {}".format(fname))
env.seed()
# Collect paths until we have enough timesteps
timesteps_this_batch = 0
paths = []
while True:
path = rollout(env,
policy,
max_pathlength,
animate=(len(paths) == 0 and (i % 10 == 0)
and animate),
obfilter=obfilter)
paths.append(path)
n = pathlength(path)
timesteps_this_batch += n
timesteps_so_far += n
if timesteps_this_batch > timesteps_per_batch:
break
# Estimate advantage function
vtargs = []
advs = []
for path in paths:
rew_t = path["reward"]
return_t = common.discount(rew_t, gamma)
vtargs.append(return_t)
vpred_t = vf.predict(path)
vpred_t = np.append(vpred_t,
0.0 if path["terminated"] else vpred_t[-1])
delta_t = rew_t + gamma * vpred_t[1:] - vpred_t[:-1]
adv_t = common.discount(delta_t, gamma * lam)
advs.append(adv_t)
# Update value function
vf.fit(paths, vtargs)
# Build arrays for policy update
ob_no = np.concatenate([path["observation"] for path in paths])
action_na = np.concatenate([path["action"] for path in paths])
oldac_dist = np.concatenate([path["action_dist"] for path in paths])
adv_n = np.concatenate(advs)
standardized_adv_n = (adv_n - adv_n.mean()) / (adv_n.std() + 1e-8)
# Policy update
do_update(ob_no, action_na, standardized_adv_n)
min_stepsize = np.float32(1e-8)
max_stepsize = np.float32(1e0)
# Adjust stepsize
kl = policy.compute_kl(ob_no, oldac_dist)
if kl > desired_kl * 2:
logger.log("kl too high")
U.eval(
tf.assign(stepsize, tf.maximum(min_stepsize, stepsize / 1.5)))
elif kl < desired_kl / 2:
logger.log("kl too low")
U.eval(
tf.assign(stepsize, tf.minimum(max_stepsize, stepsize * 1.5)))
else:
logger.log("kl just right!")
logger.record_tabular(
"EpRewMean", np.mean([path["reward"].sum() for path in paths]))
logger.record_tabular(
"EpRewSEM",
np.std([
path["reward"].sum() / np.sqrt(len(paths)) for path in paths
]))
logger.record_tabular("EpLenMean",
np.mean([pathlength(path) for path in paths]))
logger.record_tabular("KL", kl)
if callback:
callback()
logger.dump_tabular()
i += 1
coord.request_stop()
coord.join(enqueue_threads)
def learn(env, policy, vf, gamma, lam, timesteps_per_batch, num_timesteps,
animate=False, callback=None, desired_kl=0.002):
obfilter = ZFilter(env.observation_space.shape)
max_pathlength = env.spec.timestep_limit
stepsize = tf.Variable(initial_value=np.float32(np.array(0.03)), name='stepsize')
inputs, loss, loss_sampled = policy.update_info
optim = kfac.KfacOptimizer(learning_rate=stepsize, cold_lr=stepsize*(1-0.9), momentum=0.9, kfac_update=2,\
epsilon=1e-2, stats_decay=0.99, async=1, cold_iter=1,
weight_decay_dict=policy.wd_dict, max_grad_norm=None)
pi_var_list = []
for var in tf.trainable_variables():
if "pi" in var.name:
pi_var_list.append(var)
update_op, q_runner = optim.minimize(loss, loss_sampled, var_list=pi_var_list)
do_update = U.function(inputs, update_op)
U.initialize()
# start queue runners
enqueue_threads = []
coord = tf.train.Coordinator()
for qr in [q_runner, vf.q_runner]:
assert (qr != None)
enqueue_threads.extend(qr.create_threads(tf.get_default_session(), coord=coord, start=True))
i = 0
timesteps_so_far = 0
while True:
if timesteps_so_far > num_timesteps:
break
logger.log("********** Iteration %i ************"%i)
# Collect paths until we have enough timesteps
timesteps_this_batch = 0
paths = []
while True:
path = rollout(env, policy, max_pathlength, animate=(len(paths)==0 and (i % 10 == 0) and animate), obfilter=obfilter)
paths.append(path)
n = pathlength(path)
timesteps_this_batch += n
timesteps_so_far += n
if timesteps_this_batch > timesteps_per_batch:
break
# Estimate advantage function
vtargs = []
advs = []
for path in paths:
rew_t = path["reward"]
return_t = common.discount(rew_t, gamma)
vtargs.append(return_t)
vpred_t = vf.predict(path)
vpred_t = np.append(vpred_t, 0.0 if path["terminated"] else vpred_t[-1])
delta_t = rew_t + gamma*vpred_t[1:] - vpred_t[:-1]
adv_t = common.discount(delta_t, gamma * lam)
advs.append(adv_t)
# Update value function
vf.fit(paths, vtargs)
# Build arrays for policy update
ob_no = np.concatenate([path["observation"] for path in paths])
action_na = np.concatenate([path["action"] for path in paths])
oldac_dist = np.concatenate([path["action_dist"] for path in paths])
adv_n = np.concatenate(advs)
standardized_adv_n = (adv_n - adv_n.mean()) / (adv_n.std() + 1e-8)
# Policy update
do_update(ob_no, action_na, standardized_adv_n)
min_stepsize = np.float32(1e-8)
max_stepsize = np.float32(1e0)
# Adjust stepsize
kl = policy.compute_kl(ob_no, oldac_dist)
if kl > desired_kl * 2:
logger.log("kl too high")
tf.assign(stepsize, tf.maximum(min_stepsize, stepsize / 1.5)).eval()
elif kl < desired_kl / 2:
logger.log("kl too low")
tf.assign(stepsize, tf.minimum(max_stepsize, stepsize * 1.5)).eval()
else:
logger.log("kl just right!")
logger.record_tabular("EpRewMean", np.mean([path["reward"].sum() for path in paths]))
logger.record_tabular("EpRewSEM", np.std([path["reward"].sum()/np.sqrt(len(paths)) for path in paths]))
logger.record_tabular("EpLenMean", np.mean([pathlength(path) for path in paths]))
logger.record_tabular("KL", kl)
if callback:
callback()
logger.dump_tabular()
i += 1
coord.request_stop()
coord.join(enqueue_threads)
def run(self, update_counters=True):
ob = self.env.reset()
prev_ob = np.float32(np.zeros(ob.shape))
if self.obfilter: ob = self.obfilter(ob)
terminated = False
obs = []
acs = []
ac_dists = []
logps = []
rewards = []
for _ in range(self.max_pathlength):
if self.animate:
self.env.render()
state = np.concatenate([ob, prev_ob], -1)
obs.append(state)
ac, ac_dist, logp = self.policy.act(state)
acs.append(ac)
ac_dists.append(ac_dist)
logps.append(logp)
prev_ob = np.copy(ob)
scaled_ac = self.env.action_space.low + (ac + 1.) * 0.5 * (
self.env.action_space.high - self.env.action_space.low)
scaled_ac = np.clip(scaled_ac, self.env.action_space.low,
self.env.action_space.high)
ob, rew, done, _ = self.env.step(scaled_ac)
if self.obfilter: ob = self.obfilter(ob)
rewards.append(rew)
if done:
terminated = True
break
self.rewards.append(sum(rewards))
self.rewards = self.rewards[-100:]
if update_counters:
self._num_rollouts += 1
self._num_steps += len(rewards)
path = {
"observation": np.array(obs),
"terminated": terminated,
"reward": np.array(rewards),
"action": np.array(acs),
"action_dist": np.array(ac_dists),
"logp": np.array(logps)
}
rew_t = path["reward"]
value = self.policy.predict(path["observation"], path)
vtarg = common.discount(
np.append(rew_t, 0.0 if path["terminated"] else value[-1]),
self.gamma)[:-1]
vpred_t = np.append(value, 0.0 if path["terminated"] else value[-1])
delta_t = rew_t + self.gamma * vpred_t[1:] - vpred_t[:-1]
adv_GAE = common.discount(delta_t, self.gamma * self.lam)
if np.mean(self.rewards) >= self.score and not self.finished:
self.episodes_till_done = self._num_rollouts
self.frames_till_done = self._num_steps
self.finished = True
return path, vtarg, value, adv_GAE
def learn(env,
policy,
vf,
gamma,
lam,
timesteps_per_batch,
num_timesteps,
animate=False,
callback=None,
desired_kl=0.002,
lr=0.03,
momentum=0.9):
obfilter = ZFilter(env.observation_space.shape)
max_pathlength = env.spec.timestep_limit
stepsize = tf.Variable(initial_value=np.float32(np.array(lr)),
name='stepsize')
stepsize_mul = tf.placeholder(tf.float32, shape=None)
inputs, loss, loss_sampled = policy.update_info
inputs = list(inputs)
inputs.append(stepsize_mul)
optim = kfac.KfacOptimizer(learning_rate=stepsize * stepsize_mul, cold_lr=stepsize * stepsize_mul *(1-0.9), momentum=momentum, kfac_update=2,\
epsilon=1e-2, stats_decay=0.99, async=1, cold_iter=1,
weight_decay_dict=policy.wd_dict, max_grad_norm=None)
pi_var_list = []
for var in tf.trainable_variables():
if "pi" in var.name:
pi_var_list.append(var)
update_op, q_runner = optim.minimize(loss,
loss_sampled,
var_list=pi_var_list)
do_update = U.function(inputs, update_op)
grads = optim.compute_gradients(loss, pi_var_list)
grads = [g[0] for g in grads]
old_var = [
tf.Variable(initial_value=tf.zeros_like(v)) for v in pi_var_list
]
old_to_new = tf.group(
*[tf.assign(v, o) for v, o in zip(pi_var_list, old_var)])
old_from_new = tf.group(
*[tf.assign(o, v) for v, o in zip(pi_var_list, old_var)])
do_old_var = U.function([], old_var)
do_pi_var = U.function([], pi_var_list)
do_old_from_new = U.function([], old_from_new)
with tf.control_dependencies(grads):
with tf.control_dependencies([old_to_new]):
midpoint_op, q_runner_mid = optim.apply_gradients(
list(zip(grads, pi_var_list)))
do_midpoint = U.function(inputs, midpoint_op)
U.initialize()
# start queue runners
enqueue_threads = []
coord = tf.train.Coordinator()
for qr in [q_runner, vf.q_runner]:
assert (qr != None)
enqueue_threads.extend(
qr.create_threads(tf.get_default_session(),
coord=coord,
start=True))
i = 0
timesteps_so_far = 0
while True:
if timesteps_so_far > num_timesteps:
break
logger.log("********** Iteration %i ************" % i)
# Collect paths until we have enough timesteps
timesteps_this_batch = 0
paths = []
while True:
path = rollout(env,
policy,
max_pathlength,
animate=(len(paths) == 0 and (i % 10 == 0)
and animate),
obfilter=obfilter)
paths.append(path)
n = pathlength(path)
timesteps_this_batch += n
timesteps_so_far += n
if timesteps_this_batch > timesteps_per_batch:
break
# Estimate advantage function
vtargs = []
advs = []
for path in paths:
rew_t = path["reward"]
return_t = common.discount(rew_t, gamma)
vtargs.append(return_t)
vpred_t = vf.predict(path)
vpred_t = np.append(vpred_t,
0.0 if path["terminated"] else vpred_t[-1])
delta_t = rew_t + gamma * vpred_t[1:] - vpred_t[:-1]
adv_t = common.discount(delta_t, gamma * lam)
advs.append(adv_t)
# Update value function
vf.fit(paths, vtargs)
# Build arrays for policy update
ob_no = np.concatenate([path["observation"] for path in paths])
action_na = np.concatenate([path["action"] for path in paths])
oldac_dist = np.concatenate([path["action_dist"] for path in paths])
adv_n = np.concatenate(advs)
standardized_adv_n = (adv_n - adv_n.mean()) / (adv_n.std() + 1e-8)
# Policy update
do_old_from_new()
# print(do_old_var())
do_update(ob_no, action_na, standardized_adv_n, 0.5)
do_midpoint(ob_no, action_na, standardized_adv_n, 1.0)
min_stepsize = np.float32(1e-8)
max_stepsize = np.float32(1e0)
# Adjust stepsize
kl = policy.compute_kl(ob_no, oldac_dist)
# if kl > desired_kl * 2:
# logger.log("kl too high")
# tf.assign(stepsize, tf.maximum(min_stepsize, stepsize / 1.5)).eval()
# elif kl < desired_kl / 2:
# logger.log("kl too low")
# tf.assign(stepsize, tf.minimum(max_stepsize, stepsize * 1.5)).eval()
# else:
# logger.log("kl just right!")
logger.record_tabular(
"EpRewMean", np.mean([path["reward"].sum() for path in paths]))
logger.record_tabular(
"EpRewSEM",
np.std([
path["reward"].sum() / np.sqrt(len(paths)) for path in paths
]))
logger.record_tabular("EpLenMean",
np.mean([pathlength(path) for path in paths]))
logger.record_tabular("KL", kl)
if callback:
callback()
logger.dump_tabular()
i += 1
coord.request_stop()
coord.join(enqueue_threads)
def learn(env,
policy,
vf,
gamma,
lam,
timesteps_per_batch,
num_timesteps,
animate=False,
callback=None,
desired_kl=0.002,
lr=0.03,
momentum=0.9):
ob_dim, ac_dim = policy.ob_dim, policy.ac_dim
dbpi = GaussianMlpPolicy(ob_dim, ac_dim, 'dbp')
oldpi = GaussianMlpPolicy(ob_dim, ac_dim, 'oe')
dboldpi = GaussianMlpPolicy(ob_dim, ac_dim, 'doi')
# with tf.variable_scope('dbp'):
# with tf.variable_scope('oe'):
# with tf.variable_scope('doi'):
pi = policy
do_std = U.function([], [pi.std_1a, pi.logstd_1a])
kloldnew = oldpi.pd.kl(pi.pd)
dbkloldnew = dboldpi.pd.kl(dbpi.pd)
dist = meankl = tf.reduce_mean(kloldnew)
dbkl = tf.reduce_mean(dbkloldnew)
obfilter = ZFilter(env.observation_space.shape)
max_pathlength = env.spec.timestep_limit
stepsize = tf.Variable(initial_value=np.float32(np.array(lr)),
name='stepsize')
inputs, loss, loss_sampled = policy.update_info
var_list = [v for v in tf.global_variables() if "pi" in v.name]
db_var_list = [v for v in tf.global_variables() if "dbp" in v.name]
old_var_list = [v for v in tf.global_variables() if "oe" in v.name]
db_old_var_list = [v for v in tf.global_variables() if "doi" in v.name]
print(len(var_list), len(db_var_list), len(old_var_list),
len(db_old_var_list))
assign_old_eq_new = U.function(
[], [],
updates=[
tf.assign(oldv, newv)
for (oldv, newv) in zipsame(old_var_list, var_list)
])
assign_db = U.function(
[], [],
updates=[
tf.assign(db, o) for (db, o) in zipsame(db_var_list, var_list)
] + [
tf.assign(dbold, dbnew)
for (dbold, dbnew) in zipsame(db_old_var_list, old_var_list)
])
assign_old_eq_newr = U.function(
[], [],
updates=[
tf.assign(newv, oldv)
for (oldv, newv) in zipsame(old_var_list, var_list)
])
# assign_dbr = U.function([], [], updates=
# [tf.assign(o, db) for (db, o) in zipsame(db_var_list, var_list)] +
# [tf.assign(dbnew, dbold) for (dbold, dbnew) in zipsame(db_old_var_list, old_var_list)])
klgrads = tf.gradients(dist, var_list)
dbklgrads = tf.gradients(dbkl, db_var_list)
p_grads = [tf.ones_like(v) for v in dbklgrads]
get_flat = U.GetFlat(var_list)
get_old_flat = U.GetFlat(old_var_list)
set_from_flat = U.SetFromFlat(var_list)
flat_tangent2 = tf.placeholder(dtype=tf.float32,
shape=[None],
name="flat_tan2")
shapes = [var.get_shape().as_list() for var in var_list]
start = 0
tangents2 = []
for shape in shapes:
sz = U.intprod(shape)
tangents2.append(tf.reshape(flat_tangent2[start:start + sz], shape))
start += sz
gvp2 = tf.add_n([
tf.reduce_sum(g * tangent2)
for (g, tangent2) in zipsame(dbklgrads, tangents2)
])
gvp2_grads = tf.gradients(gvp2, db_var_list)
neg_term = tf.add_n([
tf.reduce_sum(g * tangent2)
for (g, tangent2) in zipsame(gvp2_grads, tangents2)
]) / 2.
ng1 = tf.gradients(neg_term, db_var_list)
ng2 = tf.gradients(neg_term, db_old_var_list)
neg_term_grads = [
a + b for (a, b) in zip(tf.gradients(neg_term, db_var_list),
tf.gradients(neg_term, db_old_var_list))
]
neg_term = neg_term_grads
# neg_term = tf.concat(axis=0, values=[tf.reshape(v, [U.numel(v)]) for v in neg_term_grads])
pos_term = tf.add_n([
tf.reduce_sum(g * tangent)
for (g, tangent) in zipsame(gvp2_grads, p_grads)
])
pos_term_grads = [
a + b for (a, b) in zip(tf.gradients(pos_term, db_var_list),
tf.gradients(pos_term, db_old_var_list))
]
pos_term_sum = tf.add_n([
tf.reduce_sum(g * tangent)
for (g, tangent) in zipsame(pos_term_grads, tangents2)
])
pos_term_grads = tf.gradients(pos_term_sum, p_grads)
pos_term = pos_term_grads
# pos_term = tf.concat(axis=0, values=[tf.reshape(v, [U.numel(v)]) for v in pos_term_grads])
geo_term = [(p - n) * 0.5 for p, n in zip(pos_term, neg_term)]
optim = kfac.KfacOptimizer(learning_rate=stepsize, cold_lr=stepsize*(1-0.9), momentum=momentum, kfac_update=2,\
epsilon=1e-2, stats_decay=0.99, async=1, cold_iter=1,
weight_decay_dict=policy.wd_dict, max_grad_norm=None)
pi_var_list = []
for var in tf.trainable_variables():
if "pi" in var.name:
pi_var_list.append(var)
grads = optim.compute_gradients(loss, var_list=pi_var_list)
update_op, q_runner = optim.minimize(loss,
loss_sampled,
var_list=pi_var_list)
geo_term = [g1 + g2[0] for g1, g2 in zip(geo_term, grads)]
geo_grads = list(zip(geo_term, var_list))
update_geo_op, q_runner_geo = optim.apply_gradients(geo_grads)
do_update = U.function(inputs, update_op)
inputs_tangent = list(inputs) + [flat_tangent2]
do_update_geo = U.function(inputs_tangent, update_geo_op)
do_get_geo_term = U.function(inputs_tangent, [ng1, ng2])
U.initialize()
# start queue runners
enqueue_threads = []
coord = tf.train.Coordinator()
for qr in [q_runner, vf.q_runner, q_runner_geo]:
assert (qr != None)
enqueue_threads.extend(
qr.create_threads(tf.get_default_session(),
coord=coord,
start=True))
i = 0
timesteps_so_far = 0
while True:
if timesteps_so_far > num_timesteps:
break
logger.log("********** Iteration %i ************" % i)
# Collect paths until we have enough timesteps
timesteps_this_batch = 0
paths = []
while True:
path = rollout(env,
policy,
max_pathlength,
animate=(len(paths) == 0 and (i % 10 == 0)
and animate),
obfilter=obfilter)
paths.append(path)
n = pathlength(path)
timesteps_this_batch += n
timesteps_so_far += n
if timesteps_this_batch > timesteps_per_batch:
break
# Estimate advantage function
vtargs = []
advs = []
for path in paths:
rew_t = path["reward"]
return_t = common.discount(rew_t, gamma)
vtargs.append(return_t)
vpred_t = vf.predict(path)
vpred_t = np.append(vpred_t,
0.0 if path["terminated"] else vpred_t[-1])
delta_t = rew_t + gamma * vpred_t[1:] - vpred_t[:-1]
adv_t = common.discount(delta_t, gamma * lam)
advs.append(adv_t)
# Update value function
vf.fit(paths, vtargs)
# Build arrays for policy update
ob_no = np.concatenate([path["observation"] for path in paths])
action_na = np.concatenate([path["action"] for path in paths])
oldac_dist = np.concatenate([path["action_dist"] for path in paths])
adv_n = np.concatenate(advs)
standardized_adv_n = (adv_n - adv_n.mean()) / (adv_n.std() + 1e-8)
assign_old_eq_new() # set old parameter values to new parameter values
assign_db()
# Policy update
do_update(ob_no, action_na, standardized_adv_n)
# ft2 = get_flat() - get_old_flat()
# assign_old_eq_newr() # assign back
# gnp = do_get_geo_term(ob_no, action_na, standardized_adv_n, ft2)
# def check_nan(bs):
# return [~np.isnan(b).all() for b in bs]
# print(gnp[0])
# print('.....asdfasdfadslfkadsjfaksdfalsdkfjaldskf')
# print(gnp[1])
# do_update_geo(ob_no, action_na, standardized_adv_n, ft2)
min_stepsize = np.float32(1e-8)
max_stepsize = np.float32(1e0)
# Adjust stepsize
kl = policy.compute_kl(ob_no, oldac_dist)
# if kl > desired_kl * 2:
# logger.log("kl too high")
# tf.assign(stepsize, tf.maximum(min_stepsize, stepsize / 1.5)).eval()
# elif kl < desired_kl / 2:
# logger.log("kl too low")
# tf.assign(stepsize, tf.minimum(max_stepsize, stepsize * 1.5)).eval()
# else:
# logger.log("kl just right!")
logger.record_tabular(
"EpRewMean", np.mean([path["reward"].sum() for path in paths]))
logger.record_tabular(
"EpRewSEM",
np.std([
path["reward"].sum() / np.sqrt(len(paths)) for path in paths
]))
logger.record_tabular("EpLenMean",
np.mean([pathlength(path) for path in paths]))
logger.record_tabular("KL", kl)
print(do_std())
if callback:
callback()
logger.dump_tabular()
i += 1
coord.request_stop()
coord.join(enqueue_threads)
def learn(env,
policy,
vf,
gamma,
lam,
timesteps_per_batch,
num_timesteps,
animate=False,
callback=None,
desired_kl=0.002,
save_path="./",
save_after=200,
load_path=None,
save_rollouts=False):
obfilter = ZFilter(env.observation_space.shape)
max_pathlength = env.spec.timestep_limit
stepsize = tf.Variable(initial_value=np.float32(np.array(0.03)),
name='stepsize')
inputs, loss, loss_sampled = policy.update_info
optim = kfac.KfacOptimizer(learning_rate=stepsize, cold_lr=stepsize*(1-0.9), momentum=0.9, kfac_update=2,\
epsilon=1e-2, stats_decay=0.99, async=1, cold_iter=1,
weight_decay_dict=policy.wd_dict, max_grad_norm=None)
pi_var_list = []
for var in tf.trainable_variables():
if "pi" in var.name:
pi_var_list.append(var)
update_op, q_runner = optim.minimize(loss,
loss_sampled,
var_list=pi_var_list)
do_update = U.function(inputs, update_op)
U.initialize()
# start queue runners
enqueue_threads = []
coord = tf.train.Coordinator()
for qr in [q_runner, vf.q_runner]:
assert (qr != None)
enqueue_threads.extend(
qr.create_threads(U.get_session(), coord=coord, start=True))
if load_path != None:
saver = tf.train.Saver()
saver.restore(U.get_session(), os.path.join(load_path, "model.ckpt"))
obfilter_path = os.path.join(load_path, "obfilter.pkl")
with open(obfilter_path, 'rb') as obfilter_input:
obfilter = pickle.load(obfilter_input)
print("Loaded Model")
else:
# create saver
saver = tf.train.Saver()
i = 0
timesteps_so_far = 0
while True:
if timesteps_so_far > num_timesteps:
break
logger.log("********** Iteration %i ************" % i)
# Collect paths until we have enough timesteps
timesteps_this_batch = 0
paths = []
while True:
#path = rollout(env, policy, max_pathlength, animate=(len(paths)==0 and (i % 10 == 0) and animate), obfilter=obfilter)
if "jaco" in env.spec.id.lower():
path = rollout(env,
policy,
max_pathlength,
animate=animate,
obfilter=obfilter,
save_rollouts=save_rollouts)
goal_dist = np.linalg.norm(env.env.env.get_body_com("jaco_link_hand") \
- env.env.env.get_body_com("target"))
if goal_dist <= 0.12:
print("goal_dist {} ; episode added".format(goal_dist))
paths.append(path)
else:
path = rollout(env,
policy,
max_pathlength,
animate=animate,
obfilter=obfilter,
save_rollouts=save_rollouts)
n = pathlength(path)
timesteps_this_batch += n
timesteps_so_far += n
if timesteps_this_batch > timesteps_per_batch:
break
if save_rollouts:
# save the rollouts
rollouts_path = os.path.join(load_path, "rollouts-v2.pkl")
with open(rollouts_path, 'wb') as rollouts_output:
pickle.dump(paths, rollouts_output, pickle.HIGHEST_PROTOCOL)
sys.exit()
# Estimate advantage function
vtargs = []
advs = []
for path in paths:
rew_t = path["reward"]
return_t = common.discount(rew_t, gamma)
vtargs.append(return_t)
vpred_t = vf.predict(path)
vpred_t = np.append(vpred_t,
0.0 if path["terminated"] else vpred_t[-1])
delta_t = rew_t + gamma * vpred_t[1:] - vpred_t[:-1]
adv_t = common.discount(delta_t, gamma * lam)
advs.append(adv_t)
# Update value function
vf.fit(paths, vtargs)
# Build arrays for policy update
ob_no = np.concatenate([path["observation"] for path in paths])
action_na = np.concatenate([path["action"] for path in paths])
oldac_dist = np.concatenate([path["action_dist"] for path in paths])
logp_n = np.concatenate([path["logp"] for path in paths])
adv_n = np.concatenate(advs)
standardized_adv_n = (adv_n - adv_n.mean()) / (adv_n.std() + 1e-8)
# Policy update
do_update(ob_no, action_na, standardized_adv_n)
min_stepsize = np.float32(1e-8)
max_stepsize = np.float32(1e0)
# Adjust stepsize
kl = policy.compute_kl(ob_no, oldac_dist)
if kl > desired_kl * 2:
logger.log("kl too high")
U.eval(
tf.assign(stepsize, tf.maximum(min_stepsize, stepsize / 1.5)))
elif kl < desired_kl / 2:
logger.log("kl too low")
U.eval(
tf.assign(stepsize, tf.minimum(max_stepsize, stepsize * 1.5)))
else:
logger.log("kl just right!")
logger.record_tabular(
"EpRewMean", np.mean([path["reward"].sum() for path in paths]))
logger.record_tabular(
"EpRewSEM",
np.std([
path["reward"].sum() / np.sqrt(len(paths)) for path in paths
]))
logger.record_tabular("EpLenMean",
np.mean([pathlength(path) for path in paths]))
logger.record_tabular("KL", kl)
if callback:
callback()
logger.dump_tabular()
# save model if necessary
if i % save_after == 0:
save(saver, obfilter, save_path)
i += 1
def learn(env,
policy,
vf,
gamma,
lam,
timesteps_per_batch,
num_timesteps,
animate=False,
callback=None,
desired_kl=0.002,
save_model_with_prefix=None,
restore_model_from_file=None,
outdir="/tmp/rosrl/experiments/continuous/acktr/"):
obfilter = ZFilter(env.observation_space.shape)
# Risto change
max_pathlength = env.max_episode_steps
stepsize = tf.Variable(initial_value=np.float32(np.array(0.03)),
name='stepsize')
inputs, loss, loss_sampled = policy.update_info
optim = kfac.KfacOptimizer(learning_rate=stepsize, cold_lr=stepsize*(1-0.9), momentum=0.9, kfac_update=2,\
epsilon=1e-2, stats_decay=0.99, async_=1, cold_iter=1,
weight_decay_dict=policy.wd_dict, max_grad_norm=None)
pi_var_list = []
for var in tf.trainable_variables():
if "pi" in var.name:
pi_var_list.append(var)
update_op, q_runner = optim.minimize(loss,
loss_sampled,
var_list=pi_var_list)
do_update = U.function(inputs, update_op)
U.initialize()
"""
Here we add a possibility to resume from a previously saved model if a model file is provided
"""
if restore_model_from_file:
saver = tf.train.Saver()
saver.restore(tf.get_default_session(), restore_model_from_file)
logger.log("Loaded model from {}".format(restore_model_from_file))
# start queue runners
enqueue_threads = []
coord = tf.train.Coordinator()
for qr in [q_runner, vf.q_runner]:
assert (qr != None)
enqueue_threads.extend(
qr.create_threads(tf.get_default_session(),
coord=coord,
start=True))
i = 0
timesteps_so_far = 0
if save_model_with_prefix:
# basePath = '/tmp/rosrl/' + str(env.__class__.__name__) +'/acktr/'
summary_writer = tf.summary.FileWriter(outdir,
graph=tf.get_default_graph())
while True:
if timesteps_so_far > num_timesteps:
break
logger.log("********** Iteration %i ************" % i)
# Collect paths until we have enough timesteps
timesteps_this_batch = 0
paths = []
while True:
path = rollout(env,
policy,
max_pathlength,
animate=(len(paths) == 0 and (i % 10 == 0)
and animate),
obfilter=obfilter)
paths.append(path)
n = pathlength(path)
timesteps_this_batch += n
timesteps_so_far += n
if timesteps_this_batch > timesteps_per_batch:
break
# Estimate advantage function
vtargs = []
advs = []
for path in paths:
rew_t = path["reward"]
return_t = common.discount(rew_t, gamma)
vtargs.append(return_t)
vpred_t = vf.predict(path)
vpred_t = np.append(vpred_t,
0.0 if path["terminated"] else vpred_t[-1])
delta_t = rew_t + gamma * vpred_t[1:] - vpred_t[:-1]
adv_t = common.discount(delta_t, gamma * lam)
advs.append(adv_t)
# Update value function
vf.fit(paths, vtargs)
# Build arrays for policy update
ob_no = np.concatenate([path["observation"] for path in paths])
action_na = np.concatenate([path["action"] for path in paths])
oldac_dist = np.concatenate([path["action_dist"] for path in paths])
adv_n = np.concatenate(advs)
standardized_adv_n = (adv_n - adv_n.mean()) / (adv_n.std() + 1e-8)
# Policy update
do_update(ob_no, action_na, standardized_adv_n)
min_stepsize = np.float32(1e-8)
max_stepsize = np.float32(1e0)
# Adjust stepsize
kl = policy.compute_kl(ob_no, oldac_dist)
if kl > desired_kl * 2:
logger.log("kl too high")
tf.assign(stepsize, tf.maximum(min_stepsize,
stepsize / 1.5)).eval()
elif kl < desired_kl / 2:
logger.log("kl too low")
tf.assign(stepsize, tf.minimum(max_stepsize,
stepsize * 1.5)).eval()
else:
logger.log("kl just right!")
logger.record_tabular(
"EpRewMean", np.mean([path["reward"].sum() for path in paths]))
logger.record_tabular(
"EpRewSEM",
np.std([
path["reward"].sum() / np.sqrt(len(paths)) for path in paths
]))
logger.record_tabular("EpLenMean",
np.mean([pathlength(path) for path in paths]))
logger.record_tabular("KL", kl)
if callback:
callback()
logger.dump_tabular()
"""
Save the model at every itteration
"""
if save_model_with_prefix:
if np.mean([path["reward"].sum() for path in paths]) > -50.0:
# basePath = '/tmp/rosrl/' + str(env.__class__.__name__) +'/acktr/'
summary = tf.Summary(value=[
tf.Summary.Value(tag="EpRewMean",
simple_value=np.mean([
path["reward"].sum() for path in paths
]))
])
summary_writer.add_summary(summary, i)
if not os.path.exists(outdir):
os.makedirs(outdir)
modelF = outdir + '/' + save_model_with_prefix + "_afterIter_" + str(
i) + ".model"
U.save_state(modelF)
logger.log("Saved model to file :{}".format(modelF))
i += 1
coord.request_stop()
coord.join(enqueue_threads)
def learn(env,
policy,
vf,
gamma,
lam,
timesteps_per_batch,
num_timesteps,
animate=False,
callback=None,
desired_kl=0.002,
fname='./training.ckpt'):
mean_logger = setup_logger("Mean Logger", "log/episode_mean.txt")
# print("Filter shape: ", env.observation_space.shape)
space = (env.observation_space.shape[0] * 2, )
obfilter = ZFilter(space)
max_pathlength = env.spec.timestep_limit
stepsize = tf.Variable(initial_value=np.float32(np.array(0.03)),
name='stepsize') #0.03
inputs, loss, loss_sampled = policy.update_info
optim = kfac.KfacOptimizer(learning_rate=stepsize, cold_lr=stepsize*(1-0.9), momentum=0.9, kfac_update=2,\
epsilon=1e-2, stats_decay=0.99, async=1, cold_iter=1,
weight_decay_dict=policy.wd_dict, max_grad_norm=None)
pi_var_list = []
for var in tf.trainable_variables():
if "pi" in var.name:
pi_var_list.append(var)
update_op, q_runner = optim.minimize(loss,
loss_sampled,
var_list=pi_var_list)
do_update = U.function(inputs, update_op)
U.initialize()
#changes
if fname != None and tf.train.checkpoint_exists(fname):
saver = tf.train.Saver()
saver.restore(tf.get_default_session(), fname)
logger.log("Model loaded from file {}".format(fname))
# start queue runners
enqueue_threads = []
coord = tf.train.Coordinator()
for qr in [q_runner, vf.q_runner]:
assert (qr != None, "QR is None")
enqueue_threads.extend(
qr.create_threads(tf.get_default_session(),
coord=coord,
start=True))
i = 0
timesteps_so_far = 0
total_reward = float()
while True:
print("Timestep Number: %d of %d" % (timesteps_so_far, num_timesteps))
if timesteps_so_far > num_timesteps:
break
logger.log("********** Iteration %i ************" % i)
#Save model every 100 iterations
if fname != None and (i % 100 == 0):
os.makedirs(os.path.dirname(fname), exist_ok=True)
saver = tf.train.Saver()
saver.save(tf.get_default_session(), fname)
logger.log("Model saved to file {}".format(fname))
env.seed()
# Collect paths until we have enough timesteps
timesteps_this_batch = 0
paths = []
terminal_rew = []
while True:
path, temp_rew = rollout(env,
policy,
max_pathlength,
animate=(len(paths) == 0 and (i % 10 == 0)
and animate),
obfilter=obfilter)
paths.append(path)
terminal_rew.append(np.array(temp_rew))
n = pathlength(path)
timesteps_this_batch += n
if timesteps_this_batch > timesteps_per_batch:
break
timesteps_so_far += 1
# Estimate advantage function
vtargs = []
advs = []
for path in paths:
rew_t = path["reward"]
return_t = common.discount(rew_t, gamma)
vtargs.append(return_t)
vpred_t = vf.predict(path)
vpred_t = np.append(vpred_t,
0.0 if path["terminated"] else vpred_t[-1])
delta_t = rew_t + gamma * vpred_t[1:] - vpred_t[:-1]
adv_t = common.discount(delta_t, gamma * lam)
advs.append(adv_t)
# Update value function
vf.fit(paths, vtargs)
# Build arrays for policy update
ob_no = np.concatenate([path["observation"] for path in paths])
action_na = np.concatenate([path["action"] for path in paths])
oldac_dist = np.concatenate([path["action_dist"] for path in paths])
adv_n = np.concatenate(advs)
standardized_adv_n = (adv_n - adv_n.mean()) / (adv_n.std() + 1e-8)
# Policy update
do_update(ob_no, action_na, standardized_adv_n)
min_stepsize = np.float32(1e-8)
max_stepsize = np.float32(1e0)
# Adjust stepsize
kl = policy.compute_kl(ob_no, oldac_dist)
if kl > desired_kl * 2:
logger.log("kl too high")
tf.assign(stepsize, tf.maximum(min_stepsize,
stepsize / 1.5)).eval()
elif kl < desired_kl / 2:
logger.log("kl too low")
tf.assign(stepsize, tf.minimum(max_stepsize,
stepsize * 1.5)).eval()
else:
logger.log("kl just right!")
terminal_rew = np.array(terminal_rew)
rew_mean = np.mean([path.sum() for path in terminal_rew])
rew_sem = np.std(
[path.sum() / np.sqrt(len(terminal_rew)) for path in terminal_rew])
len_mean = np.mean([path.shape[0] for path in terminal_rew])
# rewList = []
# for path in paths:
# trew = []
# rew_i = 0
# while True:
# trew.append(path["reward"][rew_i])
# rew_i += 11
# if rew_i > (len(path["reward"])-1):
# break
# rewList.append( np.array(trew) )
# rewList = np.array(rewList)
# rew_mean = np.mean([path.sum() for path in rewList])
# rew_sem = np.std([path.sum()/np.sqrt(len(rewList)) for path in rewList])
# len_mean = np.mean([path.shape[0] for path in rewList])
# rew_mean = np.mean([path["reward"].sum() for path in paths])
# rew_sem = np.std([path["reward"].sum()/np.sqrt(len(paths)) for path in paths])
# len_mean = np.mean([pathlength(path) for path in paths])
total_reward += rew_mean
logger.record_tabular("EpRewMean", rew_mean)
logger.record_tabular("EpRewSEM", rew_sem)
logger.record_tabular("EpLenMean", len_mean)
logger.record_tabular("TotalRewardMean", total_reward)
logger.record_tabular("KL", kl)
if callback:
callback()
logger.dump_tabular()
mean_logger.info(
"Result for episode {} of {}: Sum: {}, Average: {}, Length: {}".
format(timesteps_so_far, num_timesteps, rew_mean, rew_sem,
len_mean))
i += 1
if fname != None:
os.makedirs(os.path.dirname(fname), exist_ok=True)
saver = tf.train.Saver()
saver.save(tf.get_default_session(), fname)
logger.log("Model saved to file {}".format(fname))
env.seed()
coord.request_stop()
coord.join(enqueue_threads)
