def _helper_runningmeanstd():
comm = MPI.COMM_WORLD
np.random.seed(0)
for (triple, axis) in [
((np.random.randn(3), np.random.randn(4), np.random.randn(5)), 0),
((np.random.randn(3, 2), np.random.randn(4,
2), np.random.randn(5,
2)), 0),
((np.random.randn(2, 3), np.random.randn(2, 4), np.random.randn(2,
4)), 1)
]:
arr = np.concatenate(triple, axis=axis)
ms1 = [arr.mean(axis=axis), arr.std(axis=axis), arr.shape[axis]]
ms2 = mpi_moments(triple[comm.Get_rank()], axis=axis)
for (res_1, res_2) in zipsame(ms1, ms2):
print(res_1, res_2)
assert np.allclose(res_1, res_2)
print("ok!")
def setup_model(self):
# prevent import loops
from stable_baselines.gail.adversary import TransitionClassifier
with SetVerbosity(self.verbose):
assert issubclass(self.policy, ActorCriticPolicy), "Error: the input policy for the TRPO model must be " \
"an instance of common.policies.ActorCriticPolicy."
self.nworkers = MPI.COMM_WORLD.Get_size()
self.rank = MPI.COMM_WORLD.Get_rank()
np.set_printoptions(precision=3)
self.graph = tf.Graph()
with self.graph.as_default():
self.sess = tf_util.single_threaded_session(graph=self.graph)
if self.using_gail:
self.reward_giver = TransitionClassifier(
self.observation_space,
self.action_space,
self.hidden_size_adversary,
entcoeff=self.adversary_entcoeff)
# Construct network for new policy
self.policy_pi = self.policy(self.sess,
self.observation_space,
self.action_space,
self.n_envs,
1,
None,
reuse=False,
**self.policy_kwargs)
# Network for old policy
with tf.variable_scope("oldpi", reuse=False):
old_policy = self.policy(self.sess,
self.observation_space,
self.action_space,
self.n_envs,
1,
None,
reuse=False,
**self.policy_kwargs)
with tf.variable_scope("loss", reuse=False):
atarg = tf.placeholder(dtype=tf.float32, shape=[
None
]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32,
shape=[None]) # Empirical return
observation = self.policy_pi.obs_ph
action = self.policy_pi.pdtype.sample_placeholder([None])
kloldnew = old_policy.proba_distribution.kl(
self.policy_pi.proba_distribution)
ent = self.policy_pi.proba_distribution.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
entbonus = self.entcoeff * meanent
vferr = tf.reduce_mean(
tf.square(self.policy_pi.value_fn[:, 0] - ret))
# advantage * pnew / pold
ratio = tf.exp(
self.policy_pi.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]
self.loss_names = [
"optimgain", "meankl", "entloss", "surrgain", "entropy"
]
dist = meankl
all_var_list = tf_util.get_trainable_vars("model")
var_list = [
v for v in all_var_list
if "/vf" not in v.name and "/q/" not in v.name
]
vf_var_list = [
v for v in all_var_list
if "/pi" not in v.name and "/logstd" not in v.name
]
self.get_flat = tf_util.GetFlat(var_list, sess=self.sess)
self.set_from_flat = tf_util.SetFromFlat(var_list,
sess=self.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)
tf.summary.scalar('entropy_loss', meanent)
tf.summary.scalar('policy_gradient_loss', optimgain)
tf.summary.scalar('value_function_loss', surrgain)
tf.summary.scalar('approximate_kullback-leiber', meankl)
tf.summary.scalar(
'loss',
optimgain + meankl + entbonus + surrgain + meanent)
self.assign_old_eq_new = \
tf_util.function([], [], updates=[tf.assign(oldv, newv) for (oldv, newv) in
zipsame(tf_util.get_globals_vars("oldpi"),
tf_util.get_globals_vars("model"))])
self.compute_losses = tf_util.function(
[observation, old_policy.obs_ph, action, atarg],
losses)
self.compute_fvp = tf_util.function([
flat_tangent, observation, old_policy.obs_ph, action,
atarg
], fvp)
self.compute_vflossandgrad = tf_util.function(
[observation, old_policy.obs_ph, ret],
tf_util.flatgrad(vferr, vf_var_list))
@contextmanager
def timed(msg):
if self.rank == 0 and self.verbose >= 1:
print(colorize(msg, color='magenta'))
start_time = time.time()
yield
print(
colorize("done in {:.3f} seconds".format(
(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 /= self.nworkers
return out
tf_util.initialize(sess=self.sess)
th_init = self.get_flat()
MPI.COMM_WORLD.Bcast(th_init, root=0)
self.set_from_flat(th_init)
with tf.variable_scope("Adam_mpi", reuse=False):
self.vfadam = MpiAdam(vf_var_list, sess=self.sess)
if self.using_gail:
self.d_adam = MpiAdam(
self.reward_giver.get_trainable_variables(),
sess=self.sess)
self.d_adam.sync()
self.vfadam.sync()
with tf.variable_scope("input_info", reuse=False):
tf.summary.scalar('discounted_rewards',
tf.reduce_mean(ret))
tf.summary.scalar('learning_rate',
tf.reduce_mean(self.vf_stepsize))
tf.summary.scalar('advantage', tf.reduce_mean(atarg))
tf.summary.scalar('kl_clip_range',
tf.reduce_mean(self.max_kl))
if self.full_tensorboard_log:
tf.summary.histogram('discounted_rewards', ret)
tf.summary.histogram('learning_rate', self.vf_stepsize)
tf.summary.histogram('advantage', atarg)
tf.summary.histogram('kl_clip_range', self.max_kl)
if tf_util.is_image(self.observation_space):
tf.summary.image('observation', observation)
else:
tf.summary.histogram('observation', observation)
self.timed = timed
self.allmean = allmean
self.step = self.policy_pi.step
self.proba_step = self.policy_pi.proba_step
self.initial_state = self.policy_pi.initial_state
self.params = find_trainable_variables("model")
if self.using_gail:
self.params.extend(
self.reward_giver.get_trainable_variables())
self.summary = tf.summary.merge_all()
self.compute_lossandgrad = \
tf_util.function([observation, old_policy.obs_ph, action, atarg, ret],
[self.summary, tf_util.flatgrad(optimgain, var_list)] + losses)
def learn(self,
total_timesteps,
callback=None,
log_interval=100,
tb_log_name="PPO1",
reset_num_timesteps=True):
new_tb_log = self._init_num_timesteps(reset_num_timesteps)
callback = self._init_callback(callback)
with SetVerbosity(self.verbose), TensorboardWriter(self.graph, self.tensorboard_log, tb_log_name, new_tb_log) \
as writer:
self._setup_learn()
assert issubclass(self.policy, ActorCriticPolicy), "Error: the input policy for the PPO1 model must be " \
"an instance of common.policies.ActorCriticPolicy."
with self.sess.as_default():
self.adam.sync()
callback.on_training_start(locals(), globals())
# Prepare for rollouts
seg_gen = traj_segment_generator(self.policy_pi,
self.env,
self.timesteps_per_actorbatch,
callback=callback)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
t_start = time.time()
# rolling buffer for episode lengths
len_buffer = deque(maxlen=100)
# rolling buffer for episode rewards
reward_buffer = deque(maxlen=100)
while True:
if timesteps_so_far >= total_timesteps:
break
if self.schedule == 'constant':
cur_lrmult = 1.0
elif self.schedule == 'linear':
cur_lrmult = max(
1.0 - float(timesteps_so_far) / total_timesteps, 0)
else:
raise NotImplementedError
logger.log("********** Iteration %i ************" %
iters_so_far)
seg = seg_gen.__next__()
# Stop training early (triggered by the callback)
if not seg.get('continue_training', True): # pytype: disable=attribute-error
break
add_vtarg_and_adv(seg, self.gamma, self.lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
observations, actions = seg["observations"], seg["actions"]
atarg, tdlamret = seg["adv"], seg["tdlamret"]
# true_rew is the reward without discount
if writer is not None:
total_episode_reward_logger(
self.episode_reward, seg["true_rewards"].reshape(
(self.n_envs, -1)), seg["dones"].reshape(
(self.n_envs, -1)), writer,
self.num_timesteps)
# predicted value function before udpate
vpredbefore = seg["vpred"]
# standardized advantage function estimate
atarg = (atarg - atarg.mean()) / atarg.std()
dataset = Dataset(dict(ob=observations,
ac=actions,
atarg=atarg,
vtarg=tdlamret),
shuffle=not self.policy.recurrent)
optim_batchsize = self.optim_batchsize or observations.shape[
0]
# set old parameter values to new parameter values
self.assign_old_eq_new(sess=self.sess)
logger.log("Optimizing...")
logger.log(fmt_row(13, self.loss_names))
# Here we do a bunch of optimization epochs over the data
for k in range(self.optim_epochs):
# list of tuples, each of which gives the loss for a minibatch
losses = []
for i, batch in enumerate(
dataset.iterate_once(optim_batchsize)):
steps = (
self.num_timesteps + k * optim_batchsize +
int(i *
(optim_batchsize / len(dataset.data_map))))
if writer is not None:
# run loss backprop with summary, but once every 10 runs save the metadata
# (memory, compute time, ...)
if self.full_tensorboard_log and (1 +
k) % 10 == 0:
run_options = tf.compat.v1.RunOptions(
trace_level=tf.compat.v1.RunOptions.
FULL_TRACE)
run_metadata = tf.compat.v1.RunMetadata()
summary, grad, *newlosses = self.lossandgrad(
batch["ob"],
batch["ob"],
batch["ac"],
batch["atarg"],
batch["vtarg"],
cur_lrmult,
sess=self.sess,
options=run_options,
run_metadata=run_metadata)
writer.add_run_metadata(
run_metadata, 'step%d' % steps)
else:
summary, grad, *newlosses = self.lossandgrad(
batch["ob"],
batch["ob"],
batch["ac"],
batch["atarg"],
batch["vtarg"],
cur_lrmult,
sess=self.sess)
writer.add_summary(summary, steps)
else:
_, grad, *newlosses = self.lossandgrad(
batch["ob"],
batch["ob"],
batch["ac"],
batch["atarg"],
batch["vtarg"],
cur_lrmult,
sess=self.sess)
self.adam.update(grad,
self.optim_stepsize * cur_lrmult)
losses.append(newlosses)
logger.log(fmt_row(13, np.mean(losses, axis=0)))
logger.log("Evaluating losses...")
losses = []
for batch in dataset.iterate_once(optim_batchsize):
newlosses = self.compute_losses(batch["ob"],
batch["ob"],
batch["ac"],
batch["atarg"],
batch["vtarg"],
cur_lrmult,
sess=self.sess)
losses.append(newlosses)
mean_losses, _, _ = mpi_moments(losses, axis=0)
logger.log(fmt_row(13, mean_losses))
for (loss_val, name) in zipsame(mean_losses,
self.loss_names):
logger.record_tabular("loss_" + name, loss_val)
logger.record_tabular(
"ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
# local values
lrlocal = (seg["ep_lens"], seg["ep_rets"])
# list of tuples
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal)
lens, rews = map(flatten_lists, zip(*listoflrpairs))
len_buffer.extend(lens)
reward_buffer.extend(rews)
if len(len_buffer) > 0:
logger.record_tabular("EpLenMean", np.mean(len_buffer))
logger.record_tabular("EpRewMean",
np.mean(reward_buffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
current_it_timesteps = MPI.COMM_WORLD.allreduce(
seg["total_timestep"])
timesteps_so_far += current_it_timesteps
self.num_timesteps += current_it_timesteps
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", self.num_timesteps)
logger.record_tabular("TimeElapsed", time.time() - t_start)
if self.verbose >= 1 and MPI.COMM_WORLD.Get_rank() == 0:
logger.dump_tabular()
callback.on_training_end()
return self
def setup_model(self):
with SetVerbosity(self.verbose):
self.graph = tf.Graph()
with self.graph.as_default():
self.set_random_seed(self.seed)
self.sess = tf_util.make_session(num_cpu=self.n_cpu_tf_sess,
graph=self.graph)
# Construct network for new policy
self.policy_pi = self.policy(self.sess,
self.observation_space,
self.action_space,
self.n_envs,
1,
None,
reuse=False,
**self.policy_kwargs)
# Network for old policy
with tf.compat.v1.variable_scope("oldpi", reuse=False):
old_pi = self.policy(self.sess,
self.observation_space,
self.action_space,
self.n_envs,
1,
None,
reuse=False,
**self.policy_kwargs)
with tf.compat.v1.variable_scope("loss", reuse=False):
# Target advantage function (if applicable)
atarg = tf.compat.v1.placeholder(dtype=tf.float32,
shape=[None])
# Empirical return
ret = tf.compat.v1.placeholder(dtype=tf.float32,
shape=[None])
# learning rate multiplier, updated with schedule
lrmult = tf.compat.v1.placeholder(name='lrmult',
dtype=tf.float32,
shape=[])
# Annealed cliping parameter epislon
clip_param = self.clip_param * lrmult
obs_ph = self.policy_pi.obs_ph
action_ph = self.policy_pi.pdtype.sample_placeholder(
[None])
kloldnew = old_pi.proba_distribution.kl(
self.policy_pi.proba_distribution)
ent = self.policy_pi.proba_distribution.entropy()
meankl = tf.reduce_mean(input_tensor=kloldnew)
meanent = tf.reduce_mean(input_tensor=ent)
pol_entpen = (-self.entcoeff) * meanent
# pnew / pold
ratio = tf.exp(
self.policy_pi.proba_distribution.logp(action_ph) -
old_pi.proba_distribution.logp(action_ph))
# surrogate from conservative policy iteration
surr1 = ratio * atarg
surr2 = tf.clip_by_value(ratio, 1.0 - clip_param,
1.0 + clip_param) * atarg
# PPO's pessimistic surrogate (L^CLIP)
pol_surr = -tf.reduce_mean(
input_tensor=tf.minimum(surr1, surr2))
vf_loss = tf.reduce_mean(
input_tensor=tf.square(self.policy_pi.value_flat -
ret))
total_loss = pol_surr + pol_entpen + vf_loss
losses = [pol_surr, pol_entpen, vf_loss, meankl, meanent]
self.loss_names = [
"pol_surr", "pol_entpen", "vf_loss", "kl", "ent"
]
tf.compat.v1.summary.scalar('entropy_loss', pol_entpen)
tf.compat.v1.summary.scalar('policy_gradient_loss',
pol_surr)
tf.compat.v1.summary.scalar('value_function_loss', vf_loss)
tf.compat.v1.summary.scalar('approximate_kullback-leibler',
meankl)
tf.compat.v1.summary.scalar('clip_factor', clip_param)
tf.compat.v1.summary.scalar('loss', total_loss)
self.params = tf_util.get_trainable_vars("model")
self.assign_old_eq_new = tf_util.function(
[], [],
updates=[
tf.compat.v1.assign(oldv, newv)
for (oldv, newv) in zipsame(
tf_util.get_globals_vars("oldpi"),
tf_util.get_globals_vars("model"))
])
with tf.compat.v1.variable_scope("Adam_mpi", reuse=False):
self.adam = MpiAdam(self.params,
epsilon=self.adam_epsilon,
sess=self.sess)
with tf.compat.v1.variable_scope("input_info", reuse=False):
tf.compat.v1.summary.scalar(
'discounted_rewards', tf.reduce_mean(input_tensor=ret))
tf.compat.v1.summary.scalar(
'learning_rate',
tf.reduce_mean(input_tensor=self.optim_stepsize))
tf.compat.v1.summary.scalar(
'advantage', tf.reduce_mean(input_tensor=atarg))
tf.compat.v1.summary.scalar(
'clip_range',
tf.reduce_mean(input_tensor=self.clip_param))
if self.full_tensorboard_log:
tf.compat.v1.summary.histogram('discounted_rewards',
ret)
tf.compat.v1.summary.histogram('learning_rate',
self.optim_stepsize)
tf.compat.v1.summary.histogram('advantage', atarg)
tf.compat.v1.summary.histogram('clip_range',
self.clip_param)
if tf_util.is_image(self.observation_space):
tf.compat.v1.summary.image('observation', obs_ph)
else:
tf.compat.v1.summary.histogram(
'observation', obs_ph)
self.step = self.policy_pi.step
self.proba_step = self.policy_pi.proba_step
self.initial_state = self.policy_pi.initial_state
tf_util.initialize(sess=self.sess)
self.summary = tf.compat.v1.summary.merge_all()
self.lossandgrad = tf_util.function(
[obs_ph, old_pi.obs_ph, action_ph, atarg, ret, lrmult],
[self.summary,
tf_util.flatgrad(total_loss, self.params)] + losses)
self.compute_losses = tf_util.function(
[obs_ph, old_pi.obs_ph, action_ph, atarg, ret, lrmult],
losses)
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)
num_options=2,
app='',
saves=False,
wsaves=False,
epoch=-1,
seed=1,
dc=0):
optim_batchsize_ideal = optim_batchsize
np.random.seed(seed)
tf.set_random_seed(seed)
# env._seed(seed)
gamename = env.spec.id[:-3].lower()
gamename += 'seed' + str(seed)
gamename += app
dirname = '{}_{}opts_saves/'.format(gamename, num_options)
if wsaves:
first = True
if not os.path.exists(dirname):
os.makedirs(dirname)
first = False
# while os.path.exists(dirname) and first:
# dirname += '0'
files = ['pposgd_simple.py', 'mlp_policy.py', 'run_main.py']
for i in range(len(files)):
src = os.path.expanduser('~/baselines/baselines/ppo1/') + files[i]
dest = os.path.expanduser('~/baselines/baselines/ppo1/') + dirname
shutil.copy2(src, dest)
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_func("pi", ob_space,
ac_space) # Construct network for new policy
oldpi = policy_func("oldpi", ob_space, ac_space) # Network for old policy
atarg = tf.placeholder(
dtype=tf.float32,
shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
# option = tf.placeholder(dtype=tf.int32, shape=[None])
lrmult = tf.placeholder(
name='lrmult', dtype=tf.float32,
shape=[]) # learning rate multiplier, updated with schedule
clip_param = clip_param * lrmult # Annealed cliping parameter epislon
# pdb.set_trace()
ob = U.get_placeholder_cached(name="ob")
option = U.get_placeholder_cached(name="option")
term_adv = U.get_placeholder(name='term_adv',
dtype=tf.float32,
shape=[None])
ac = pi.pdtype.sample_placeholder([None])
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
pol_entpen = (-entcoeff) * meanent
ratio = tf.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac)) # pnew / pold
surr1 = ratio * atarg # surrogate from conservative policy iteration
surr2 = tf.clip_by_value(ratio, 1.0 - clip_param,
1.0 + clip_param) * atarg #
pol_surr = -tf.reduce_mean(tf.minimum(
surr1, surr2)) # PPO's pessimistic surrogate (L^CLIP)
vf_loss = tf.reduce_mean(tf.square(pi.vpred - ret))
total_loss = pol_surr + pol_entpen + vf_loss
losses = [pol_surr, pol_entpen, vf_loss, meankl, meanent]
loss_names = ["pol_surr", "pol_entpen", "vf_loss", "kl", "ent"]
term_loss = pi.tpred * term_adv
log_pi = tf.log(tf.clip_by_value(pi.op_pi, 1e-20, 1.0))
entropy = -tf.reduce_sum(pi.op_pi * log_pi, reduction_indices=1)
op_loss = -tf.reduce_sum(log_pi[0][option[0]] * atarg + entropy * 0.1)
total_loss += op_loss
var_list = pi.get_trainable_variables()
term_list = var_list[6:8]
lossandgrad = U.function([ob, ac, atarg, ret, lrmult, option, term_adv],
losses + [U.flatgrad(total_loss, var_list)])
termloss = U.function([ob, option, term_adv],
[U.flatgrad(term_loss, var_list)
]) # Since we will use a different step size.
adam = MpiAdam(var_list, epsilon=adam_epsilon)
assign_old_eq_new = U.function(
[], [],
updates=[
tf.assign(oldv, newv)
for (oldv,
newv) in zipsame(oldpi.get_variables(), pi.get_variables())
])
compute_losses = U.function([ob, ac, atarg, ret, lrmult, option], losses)
U.initialize()
adam.sync()
saver = tf.train.Saver(max_to_keep=10000)
results = []
if saves:
results = open(
gamename + '_' + str(num_options) + 'opts_' + '_results.csv', 'w')
out = 'epoch,avg_reward'
for opt in range(num_options):
out += ',option {} dur'.format(opt)
for opt in range(num_options):
out += ',option {} std'.format(opt)
for opt in range(num_options):
out += ',option {} term'.format(opt)
for opt in range(num_options):
out += ',option {} adv'.format(opt)
out += '\n'
results.write(out)
# results.write('epoch,avg_reward,option 1 dur, option 2 dur, option 1 term, option 2 term\n')
results.flush()
if epoch >= 0:
dirname = '{}_{}opts_saves/'.format(gamename, num_options)
print("Loading weights from iteration: " + str(epoch))
filename = dirname + '{}_epoch_{}.ckpt'.format(gamename, epoch)
saver.restore(U.get_session(), filename)
episodes_so_far = 0
timesteps_so_far = 0
global iters_so_far
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"
seg_gen = traj_segment_generator(pi,
env,
timesteps_per_batch,
stochastic=True,
num_options=num_options,
saves=saves,
results=results,
rewbuffer=rewbuffer,
dc=dc)
datas = [0 for _ in range(num_options)]
while True:
if callback: callback(locals(), globals())
if max_timesteps and timesteps_so_far >= max_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
elif max_seconds and time.time() - tstart >= max_seconds:
break
if schedule == 'constant':
cur_lrmult = 1.0
elif schedule == 'linear':
cur_lrmult = max(1.0 - float(timesteps_so_far) / max_timesteps, 0)
else:
raise NotImplementedError
logger.log("********** Iteration %i ************" % iters_so_far)
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, gamma, lam)
opt_d = []
for i in range(num_options):
dur = np.mean(
seg['opt_dur'][i]) if len(seg['opt_dur'][i]) > 0 else 0.
opt_d.append(dur)
std = []
for i in range(num_options):
logstd = np.mean(
seg['logstds'][i]) if len(seg['logstds'][i]) > 0 else 0.
std.append(np.exp(logstd))
print("mean opt dur:", opt_d)
print("mean op pol:", np.mean(np.array(seg['optpol_p']), axis=0))
print("mean term p:", np.mean(np.array(seg['term_p']), axis=0))
print("mean value val:", np.mean(np.array(seg['value_val']), axis=0))
ob, ac, opts, atarg, tdlamret = seg["ob"], seg["ac"], seg["opts"], 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
assign_old_eq_new() # set old parameter values to new parameter values
if iters_so_far % 5 == 0 and wsaves:
print("weights are saved...")
filename = dirname + '{}_epoch_{}.ckpt'.format(
gamename, iters_so_far)
save_path = saver.save(U.get_session(), filename)
min_batch = 160
t_advs = [[] for _ in range(num_options)]
for opt in range(num_options):
indices = np.where(opts == opt)[0]
print("batch size:", indices.size)
opt_d[opt] = indices.size
if not indices.size:
t_advs[opt].append(0.)
continue
# This part is only necessasry when we use options.
# We proceed to these verifications in order not to discard any collected trajectories.
if datas[opt] != 0:
if (indices.size < min_batch and datas[opt].n > min_batch):
datas[opt] = Dataset(dict(ob=ob[indices],
ac=ac[indices],
atarg=atarg[indices],
vtarg=tdlamret[indices]),
shuffle=not pi.recurrent)
t_advs[opt].append(0.)
continue
elif indices.size + datas[opt].n < min_batch:
# pdb.set_trace()
oldmap = datas[opt].data_map
cat_ob = np.concatenate((oldmap['ob'], ob[indices]))
cat_ac = np.concatenate((oldmap['ac'], ac[indices]))
cat_atarg = np.concatenate(
(oldmap['atarg'], atarg[indices]))
cat_vtarg = np.concatenate(
(oldmap['vtarg'], tdlamret[indices]))
datas[opt] = Dataset(dict(ob=cat_ob,
ac=cat_ac,
atarg=cat_atarg,
vtarg=cat_vtarg),
shuffle=not pi.recurrent)
t_advs[opt].append(0.)
continue
elif (indices.size + datas[opt].n > min_batch and datas[opt].n
< min_batch) or (indices.size > min_batch
and datas[opt].n < min_batch):
oldmap = datas[opt].data_map
cat_ob = np.concatenate((oldmap['ob'], ob[indices]))
cat_ac = np.concatenate((oldmap['ac'], ac[indices]))
cat_atarg = np.concatenate(
(oldmap['atarg'], atarg[indices]))
cat_vtarg = np.concatenate(
(oldmap['vtarg'], tdlamret[indices]))
datas[opt] = d = Dataset(dict(ob=cat_ob,
ac=cat_ac,
atarg=cat_atarg,
vtarg=cat_vtarg),
shuffle=not pi.recurrent)
if (indices.size > min_batch and datas[opt].n > min_batch):
datas[opt] = d = Dataset(dict(ob=ob[indices],
ac=ac[indices],
atarg=atarg[indices],
vtarg=tdlamret[indices]),
shuffle=not pi.recurrent)
elif datas[opt] == 0:
datas[opt] = d = Dataset(dict(ob=ob[indices],
ac=ac[indices],
atarg=atarg[indices],
vtarg=tdlamret[indices]),
shuffle=not pi.recurrent)
optim_batchsize = optim_batchsize or ob.shape[0]
optim_epochs = np.clip(
np.int(10 * (indices.size /
(timesteps_per_batch / num_options))), 10,
10) if num_options > 1 else optim_epochs
print("optim epochs:", optim_epochs)
logger.log("Optimizing...")
# Here we do a bunch of optimization epochs over the data
for _ in range(optim_epochs):
losses = [
] # list of tuples, each of which gives the loss for a minibatch
for batch in d.iterate_once(optim_batchsize):
tadv, nodc_adv = pi.get_term_adv(batch["ob"], [opt])
tadv = tadv if num_options > 1 else np.zeros_like(tadv)
t_advs[opt].append(nodc_adv)
*newlosses, grads = lossandgrad(batch["ob"], batch["ac"],
batch["atarg"],
batch["vtarg"], cur_lrmult,
[opt], tadv)
termg = termloss(batch["ob"], [opt], tadv)
adam.update(termg[0], 5e-7 * cur_lrmult)
adam.update(grads, optim_stepsize * cur_lrmult)
losses.append(newlosses)
lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
if MPI.COMM_WORLD.Get_rank() == 0:
logger.dump_tabular()
if saves:
out = "{},{}"
for _ in range(num_options):
out += ",{},{},{},{}"
out += "\n"
info = [iters_so_far, np.mean(rewbuffer)]
for i in range(num_options):
info.append(opt_d[i])
for i in range(num_options):
info.append(std[i])
for i in range(num_options):
info.append(np.mean(np.array(seg['term_p']), axis=0)[i])
for i in range(num_options):
info.append(np.mean(t_advs[i]))
results.write(out.format(*info))
results.flush()
def setup_model(self):
# prevent import loops
with SetVerbosity(self.verbose):
assert issubclass(self.policy, ActorCriticPolicy), "Error: the input policy for the TRPO model must be " \
"an instance of common.policies.ActorCriticPolicy."
self.nworkers = MPI.COMM_WORLD.Get_size()
print("number of workers are", self.nworkers)
self.rank = MPI.COMM_WORLD.Get_rank()
np.set_printoptions(precision=3)
self.graph = tf.Graph()
with self.graph.as_default():
self.sess = tf_util.single_threaded_session(graph=self.graph)
self._setup_learn(self.seed)
# Construct network for new policy
self.policy_pi = self.policy(self.sess,
self.observation_space,
self.action_space,
self.n_envs,
1,
None,
reuse=False,
**self.policy_kwargs)
# Network for old policy
with tf.variable_scope("oldpi", reuse=False):
old_policy = self.policy(self.sess,
self.observation_space,
self.action_space,
self.n_envs,
1,
None,
reuse=False,
**self.policy_kwargs)
# Network for phi
with tf.variable_scope("phi", reuse=False):
self.policy_phi = self.policy(self.sess,
self.observation_space,
self.action_space,
self.n_envs,
1,
None,
reuse=False,
**self.policy_kwargs)
# Network for phi old
with tf.variable_scope("oldphi", reuse=False):
self.policy_phi_old = self.policy(self.sess,
self.observation_space,
self.action_space,
self.n_envs,
1,
None,
reuse=False,
**self.policy_kwargs)
with tf.variable_scope("loss", reuse=False):
atarg = tf.placeholder(dtype=tf.float32, shape=[
None
]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32,
shape=[None]) # Empirical return
observation = self.policy_pi.obs_ph
action = self.policy_pi.pdtype.sample_placeholder([None])
kloldnew = old_policy.proba_distribution.kl(
self.policy_pi.proba_distribution)
#kloldnew = self.policy_pi.proba_distribution.kl(old_policy.proba_distribution)
ent = self.policy_pi.proba_distribution.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
entbonus = self.entcoeff * meanent
vferr = tf.reduce_mean(
tf.square(self.policy_pi.value_flat - ret))
vf_phi_err = tf.reduce_mean(
tf.square(self.policy_phi.value_flat - ret))
vf_phi_old_err = tf.reduce_mean(
tf.square(self.policy_phi_old.value_flat))
# advantage * pnew / pold
ratio = tf.exp(
self.policy_pi.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]
self.loss_names = [
"optimgain", "meankl", "entloss", "surrgain", "entropy"
]
dist = meankl
all_var_list = tf_util.get_trainable_vars("model")
var_list = [
v for v in all_var_list
if "/vf" not in v.name and "/q/" not in v.name
]
vf_var_list = [
v for v in all_var_list
if "/pi" not in v.name and "/logstd" not in v.name
]
all_var_oldpi_list = tf_util.get_trainable_vars("oldpi")
var_oldpi_list = [
v for v in all_var_oldpi_list
if "/vf" not in v.name and "/q/" not in v.name
]
all_var_phi_list = tf_util.get_trainable_vars("phi")
vf_phi_var_list = [
v for v in all_var_phi_list if "/pi" not in v.name
and "/logstd" not in v.name and "/q" not in v.name
]
all_var_phi_old_list = tf_util.get_trainable_vars("oldphi")
vf_phi_old_var_list = [
v for v in all_var_phi_old_list if "/pi" not in v.name
and "/logstd" not in v.name and "/q" not in v.name
]
#print("vars", vf_var_list)
self.policy_vars = all_var_list
self.oldpolicy_vars = all_var_oldpi_list
print("all var list", all_var_list)
print("phi vars", vf_phi_var_list)
print("phi old vars", vf_phi_old_var_list)
self.get_flat = tf_util.GetFlat(var_list, sess=self.sess)
self.set_from_flat = tf_util.SetFromFlat(var_list,
sess=self.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)
tf.summary.scalar('entropy_loss', meanent)
tf.summary.scalar('policy_gradient_loss', optimgain)
tf.summary.scalar('value_function_loss', surrgain)
tf.summary.scalar('approximate_kullback-leibler', meankl)
tf.summary.scalar(
'loss',
optimgain + meankl + entbonus + surrgain + meanent)
self.assign_old_eq_new = \
tf_util.function([], [], updates=[tf.assign(oldv, newv) for (oldv, newv) in
zipsame(tf_util.get_globals_vars("oldpi"),
tf_util.get_globals_vars("model"))])
self.compute_losses = tf_util.function(
[observation, old_policy.obs_ph, action, atarg],
losses)
self.compute_fvp = tf_util.function([
flat_tangent, observation, old_policy.obs_ph, action,
atarg
], fvp)
self.compute_vflossandgrad = tf_util.function(
[observation, old_policy.obs_ph, ret],
tf_util.flatgrad(vferr, vf_var_list))
self.compute_vf_phi_lossandgrad = tf_util.function(
[observation, self.policy_phi.obs_ph, ret],
tf_util.flatgrad(vf_phi_err, vf_phi_var_list))
self.compute_vf_loss = tf_util.function(
[observation, old_policy.obs_ph, ret], vferr)
self.compute_vf_phi_loss = tf_util.function(
[observation, self.policy_phi.obs_ph, ret], vf_phi_err)
#self.compute_vf_phi_old_loss = tf_util.function([self.policy_phi_old.obs_ph], vf_phi_old_err)
#self.phi_old_obs = np.array([-0.012815 , -0.02076313, 0.07524705, 0.09407324, 0.0901745 , -0.09339058, 0.03544853, -0.03297224])
#self.phi_old_obs = self.phi_old_obs.reshape((1, 8))
update_phi_old_expr = []
for var, var_target in zip(
sorted(vf_phi_var_list, key=lambda v: v.name),
sorted(vf_phi_old_var_list, key=lambda v: v.name)):
update_phi_old_expr.append(var_target.assign(var))
update_phi_old_expr = tf.group(*update_phi_old_expr)
self.update_phi_old = tf_util.function(
[], [], updates=[update_phi_old_expr])
@contextmanager
def timed(msg):
if self.rank == 0 and self.verbose >= 1:
print(colorize(msg, color='magenta'))
start_time = time.time()
yield
print(
colorize("done in {:.3f} seconds".format(
(time.time() - start_time)),
color='magenta'))
else:
yield
@contextmanager
def temp_seed(seed):
state = np.random.get_state()
np.random.seed(seed)
try:
yield
finally:
np.random.set_state(state)
def allmean(arr):
assert isinstance(arr, np.ndarray)
out = np.empty_like(arr)
MPI.COMM_WORLD.Allreduce(arr, out, op=MPI.SUM)
out /= self.nworkers
return out
tf_util.initialize(sess=self.sess)
th_init = self.get_flat()
MPI.COMM_WORLD.Bcast(th_init, root=0)
self.set_from_flat(th_init)
with tf.variable_scope("Adam_mpi", reuse=False):
self.vfadam = MpiAdam(vf_var_list, sess=self.sess)
self.vf_phi_adam = MpiAdam(vf_phi_var_list, sess=self.sess)
self.vfadam.sync()
self.vf_phi_adam.sync()
with tf.variable_scope("input_info", reuse=False):
tf.summary.scalar('discounted_rewards',
tf.reduce_mean(ret))
tf.summary.scalar('learning_rate',
tf.reduce_mean(self.vf_stepsize))
tf.summary.scalar('advantage', tf.reduce_mean(atarg))
tf.summary.scalar('kl_clip_range',
tf.reduce_mean(self.max_kl))
self.timed = timed
self.allmean = allmean
self.temp_seed = temp_seed
self.step = self.policy_pi.step
self.proba_step = self.policy_pi.proba_step
self.initial_state = self.policy_pi.initial_state
self.params = tf_util.get_trainable_vars(
"model") + tf_util.get_trainable_vars("oldpi")
self.summary = tf.summary.merge_all()
self.compute_lossandgrad = \
tf_util.function([observation, old_policy.obs_ph, action, atarg, ret],
[self.summary, tf_util.flatgrad(optimgain, var_list)] + losses)
def setup_model(self):
# prevent import loops
from stable_baselines.gail.adversary import TransitionClassifier
with SetVerbosity(self.verbose):
assert issubclass(self.policy, ActorCriticPolicy), "Error: the input policy for the TRPO model must be " \
"an instance of common.policies.ActorCriticPolicy."
self.nworkers = MPI.COMM_WORLD.Get_size()
self.rank = MPI.COMM_WORLD.Get_rank()
np.set_printoptions(precision=3)
self.graph = tf.Graph()
with self.graph.as_default():
self.set_random_seed(self.seed)
self.sess = tf_util.make_session(num_cpu=self.n_cpu_tf_sess, graph=self.graph)
if self.using_gail:
self.reward_giver = TransitionClassifier(self.observation_space, self.action_space,
self.hidden_size_adversary,
entcoeff=self.adversary_entcoeff)
# Penalty related variable
with tf.variable_scope('penalty'):
cur_cost_ph = tf.placeholder(dtype=tf.float32, shape=[None]) # episodic cost
param_init = np.log(max(np.exp(self.penalty_init) - 1, 1e-8))
penalty_param = tf.get_variable('penalty_param',
initializer=float(param_init),
trainable=True,
dtype=tf.float32)
penalty = tf.nn.softplus(penalty_param)
penalty_loss = tf.reduce_mean(-penalty_param * (cur_cost_ph - self.cost_lim))
# Construct network for new policy
self.policy_pi = self.policy(self.sess, self.observation_space, self.action_space, self.n_envs, 1,
None, reuse=False, **self.policy_kwargs)
# Network for old policy
with tf.variable_scope("oldpi", reuse=False):
old_policy = self.policy(self.sess, self.observation_space, self.action_space, self.n_envs, 1,
None, reuse=False, **self.policy_kwargs)
# # Network for safety value function
# with tf.variable_Scope("vc",reuse=False):
# self.cost_value = MLPValue(self.sess, self.observation_spacem, self.n_envs, 1, None)
with tf.variable_scope("loss", reuse=False):
atarg = tf.placeholder(dtype=tf.float32, shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
catarg = tf.placeholder(dtype=tf.float32, shape=[None]) # Target cost advantage function
cret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical cost
observation = self.policy_pi.obs_ph
action = self.policy_pi.pdtype.sample_placeholder([None])
kloldnew = old_policy.proba_distribution.kl(self.policy_pi.proba_distribution)
ent = self.policy_pi.proba_distribution.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
entbonus = self.entcoeff * meanent
vferr = tf.reduce_mean(tf.square(self.policy_pi.value_flat - ret))
vcerr = tf.reduce_mean(tf.square(self.policy_pi.vcf_flat - cret))
# advantage * pnew / pold
ratio = tf.exp(self.policy_pi.proba_distribution.logp(action) -
old_policy.proba_distribution.logp(action))
surrgain = tf.reduce_mean(ratio * atarg)
# Surrogate for cost function
surrcost = tf.reduce_mean(ratio * catarg)
optimgain = surrgain + entbonus
# Include surr_cost in pi_objective
optimgain -= penalty * surrcost
optimgain /= (1 + penalty)
# # Loss function for pi is negative of pi_objective
# optimgain = -optimgain # Should we??
losses = [optimgain, meankl, entbonus, surrgain, meanent, surrcost]
self.loss_names = ["optimgain", "meankl", "entloss", "surrgain", "entropy", "surrcost"]
dist = meankl
all_var_list = tf_util.get_trainable_vars("model")
var_list = [v for v in all_var_list if "/vf" not in v.name and "/q/" not in v.name and "/vcf" not in v.name] # policy parameters
vf_var_list = [v for v in all_var_list if "/pi" not in v.name and "/logstd" not in v.name and "/vcf" not in v.name] # value parameters
vcf_var_list = [v for v in all_var_list if "/pi" not in v.name and "/logstd" not in v.name and "/vf" not in v.name] # cost value parameters
self.get_flat = tf_util.GetFlat(var_list, sess=self.sess)
self.set_from_flat = tf_util.SetFromFlat(var_list, sess=self.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
# Fisher vector products
fvp = tf_util.flatgrad(gvp, var_list)
tf.summary.scalar('penalty_loss', penalty_loss)
tf.summary.scalar('entropy_loss', meanent)
tf.summary.scalar('policy_gradient_loss', optimgain)
tf.summary.scalar('value_function_loss', surrgain)
tf.summary.scalar('constraint_cost_function_loss', surrcost)
tf.summary.scalar('approximate_kullback-leibler', meankl)
tf.summary.scalar('loss', optimgain + meankl + entbonus + surrgain + meanent + surrcost + penalty_loss)
self.assign_old_eq_new = \
tf_util.function([], [], updates=[tf.assign(oldv, newv) for (oldv, newv) in
zipsame(tf_util.get_globals_vars("oldpi"),
tf_util.get_globals_vars("model"))])
self.compute_losses = tf_util.function([observation, old_policy.obs_ph, action, atarg, catarg], losses)
self.compute_fvp = tf_util.function([flat_tangent, observation, old_policy.obs_ph, action, atarg, catarg],
fvp) # Why need all inputs? Might for implementation easiness
# self.compute_vflossandgrad = tf_util.function([observation, old_policy.obs_ph, ret],
# tf_util.flatgrad(vferr, vf_var_list)) # Why need old_policy.obs_ph? Doesn't seem to be used
# self.compute_vcflossandgrad = tf_util.function([observation, old_policy.obs_ph, cret],
# tf_util.flatgrad(vcerr, vcf_var_list))
self.compute_vflossandgrad = tf_util.function([observation, old_policy.obs_ph, ret, cret],
[tf_util.flatgrad(vferr, vf_var_list), tf_util.flatgrad(vcerr, vcf_var_list)])
self.compute_lagrangiangrad = tf_util.function([cur_cost_ph],
tf_util.flatgrad(penalty_loss, [penalty_param]))
@contextmanager
def timed(msg):
if self.rank == 0 and self.verbose >= 1:
print(colorize(msg, color='magenta'))
start_time = time.time()
yield
print(colorize("done in {:.3f} seconds".format((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 /= self.nworkers
return out
tf_util.initialize(sess=self.sess)
th_init = self.get_flat()
MPI.COMM_WORLD.Bcast(th_init, root=0)
self.set_from_flat(th_init)
with tf.variable_scope("Adam_mpi", reuse=False):
self.vfadam = MpiAdam(vf_var_list, sess=self.sess)
if self.using_gail:
self.d_adam = MpiAdam(self.reward_giver.get_trainable_variables(), sess=self.sess)
self.d_adam.sync()
self.vfadam.sync()
# optimizer for constraint costs value function
self.vcadam = MpiAdam(vcf_var_list, sess=self.sess)
self.vcadam.sync()
# optimizer for lagragian value of safe RL
self.penaltyadam = MpiAdam([penalty_param], sess=self.sess)
self.penaltyadam.sync()
with tf.variable_scope("input_info", reuse=False):
tf.summary.scalar('discounted_rewards', tf.reduce_mean(ret))
tf.summary.scalar('discounted_costs', tf.reduce_mean(cret))
tf.summary.scalar('learning_rate', tf.reduce_mean(self.vf_stepsize))
tf.summary.scalar('advantage', tf.reduce_mean(atarg))
tf.summary.scalar('cost_advantage', tf.reduce_mean(catarg))
tf.summary.scalar('kl_clip_range', tf.reduce_mean(self.max_kl))
if self.full_tensorboard_log:
tf.summary.histogram('discounted_rewards', ret)
tf.summary.histogram('discounted_rewards', cret)
tf.summary.histogram('learning_rate', self.vf_stepsize)
tf.summary.histogram('penalty_learning_rate', self.penalty_lr)
tf.summary.histogram('advantage', atarg)
tf.summary.histogram('cost_advantage', catarg)
tf.summary.histogram('kl_clip_range', self.max_kl)
if tf_util.is_image(self.observation_space):
tf.summary.image('observation', observation)
else:
tf.summary.histogram('observation', observation)
self.timed = timed
self.allmean = allmean
self.step = self.policy_pi.step
self.proba_step = self.policy_pi.proba_step
self.initial_state = self.policy_pi.initial_state
self.params = tf_util.get_trainable_vars("model") + tf_util.get_trainable_vars("oldpi")
if self.using_gail:
self.params.extend(self.reward_giver.get_trainable_variables())
self.summary = tf.summary.merge_all()
self.compute_lossandgrad = \
tf_util.function([observation, old_policy.obs_ph, action, atarg, catarg, ret, cret, cur_cost_ph],
[self.summary, tf_util.flatgrad(optimgain, var_list)] + losses)
def learn(self,
total_timesteps,
callback=None,
seed=None,
log_interval=100,
tb_log_name="PPO1",
reset_num_timesteps=True):
new_tb_log = self._init_num_timesteps(reset_num_timesteps)
with SetVerbosity(self.verbose), TensorboardWriter(self.graph, self.tensorboard_log, tb_log_name, new_tb_log) \
as writer:
self._setup_learn(seed)
assert issubclass(self.policy, ActorCriticPolicy), "Error: the input policy for the PPO1 model must be " \
"an instance of common.policies.ActorCriticPolicy({}).".format(self.policy)
with self.sess.as_default():
self.adam.sync()
trajectory_dic = None
# Prepare for rollouts
seg_gen = traj_segment_generator(self.policy_pi, self.env,
self.timesteps_per_actorbatch)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
t_start = time.time()
# rolling buffer for episode lengths
lenbuffer = deque(maxlen=100)
# rolling buffer for episode rewards
rewbuffer = deque(maxlen=100)
self.episode_reward = np.zeros((self.n_envs, ))
if self.save_trajectory:
hidden_list = []
obs_list = []
act_list = []
rwds_list = []
dones_list = []
while True:
if callback is not None:
# Only stop training if return value is False, not when it is None. This is for backwards
# compatibility with callbacks that have no return statement.
if callback(locals(), globals()) is False:
break
if total_timesteps and timesteps_so_far >= total_timesteps:
break
if self.schedule == 'constant':
cur_lrmult = 1.0
elif self.schedule == 'linear':
cur_lrmult = max(
1.0 - float(timesteps_so_far) / total_timesteps, 0)
else:
raise NotImplementedError
# logger.log("********** Iteration %i ************" % iters_so_far)
logger.log("********** Iteration %i %i************" %
(iters_so_far, self.n_envs))
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))
obs_ph, hiddens_ph, action_ph, atarg, tdlamret = seg[
"ob"], seg["hiddens"], seg["ac"], seg["adv"], seg[
"tdlamret"]
# print(">>>hiddens_ph:",len(hiddens_ph))
if self.save_trajectory:
rwds_ph, dones_ph = seg["rew"], seg["dones"]
obs_list.append(obs_ph.copy())
hidden_list.append(hiddens_ph.copy())
act_list.append(action_ph.copy())
rwds_list.append(rwds_ph.copy())
dones_list.append(dones_ph.copy())
# true_rew is the reward without discount
if writer is not None:
self.episode_reward = total_episode_reward_logger(
self.episode_reward, seg["true_rew"].reshape(
(self.n_envs, -1)), seg["dones"].reshape(
(self.n_envs, -1)), writer,
self.num_timesteps)
# predicted value function before udpate
vpredbefore = seg["vpred"]
# standardized advantage function estimate
atarg = (atarg - atarg.mean()) / atarg.std()
dataset = Dataset(dict(ob=obs_ph,
ac=action_ph,
atarg=atarg,
vtarg=tdlamret),
shuffle=not self.policy.recurrent)
optim_batchsize = self.optim_batchsize or obs_ph.shape[0]
# set old parameter values to new parameter values
self.assign_old_eq_new(sess=self.sess)
logger.log("Optimizing...")
logger.log(fmt_row(13, self.loss_names))
# Here we do a bunch of optimization epochs over the data
for k in range(self.optim_epochs):
# list of tuples, each of which gives the loss for a minibatch
losses = []
for i, batch in enumerate(
dataset.iterate_once(optim_batchsize)):
steps = (
self.num_timesteps + k * optim_batchsize +
int(i *
(optim_batchsize / len(dataset.data_map))))
if writer is not None:
# run loss backprop with summary, but once every 10 runs save the metadata
# (memory, compute time, ...)
if self.full_tensorboard_log and (1 +
k) % 10 == 0:
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
summary, grad, *newlosses = self.lossandgrad(
batch["ob"],
batch["ob"],
batch["ac"],
batch["atarg"],
batch["vtarg"],
cur_lrmult,
sess=self.sess,
options=run_options,
run_metadata=run_metadata)
writer.add_run_metadata(
run_metadata, 'step%d' % steps)
else:
summary, grad, *newlosses = self.lossandgrad(
batch["ob"],
batch["ob"],
batch["ac"],
batch["atarg"],
batch["vtarg"],
cur_lrmult,
sess=self.sess)
writer.add_summary(summary, steps)
else:
_, grad, *newlosses = self.lossandgrad(
batch["ob"],
batch["ob"],
batch["ac"],
batch["atarg"],
batch["vtarg"],
cur_lrmult,
sess=self.sess)
self.adam.update(grad,
self.optim_stepsize * cur_lrmult)
losses.append(newlosses)
logger.log(fmt_row(13, np.mean(losses, axis=0)))
logger.log("Evaluating losses...")
losses = []
for batch in dataset.iterate_once(optim_batchsize):
newlosses = self.compute_losses(batch["ob"],
batch["ob"],
batch["ac"],
batch["atarg"],
batch["vtarg"],
cur_lrmult,
sess=self.sess)
losses.append(newlosses)
mean_losses, _, _ = mpi_moments(losses, axis=0)
logger.log(fmt_row(13, mean_losses))
for (loss_val, name) in zipsame(mean_losses,
self.loss_names):
logger.record_tabular("loss_" + name, loss_val)
logger.record_tabular(
"ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
# local values
lrlocal = (seg["ep_lens"], seg["ep_rets"])
# list of tuples
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal)
lens, rews = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
if len(lenbuffer) > 0:
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)
current_it_timesteps = MPI.COMM_WORLD.allreduce(
seg["total_timestep"])
timesteps_so_far += current_it_timesteps
self.num_timesteps += current_it_timesteps
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", self.num_timesteps)
logger.record_tabular("TimeElapsed", time.time() - t_start)
if self.verbose >= 1 and MPI.COMM_WORLD.Get_rank() == 0:
logger.dump_tabular()
if self.save_trajectory:
length = np.vstack(obs_list).shape[0]
print("Save trajectory...(length:{})".format(length))
trajectory_dic = {
"all_obvs": np.vstack(obs_list).reshape(length, -1),
"all_hiddens":
np.vstack(hidden_list).reshape(length, -1),
"all_acts": np.vstack(act_list).reshape(length, -1),
"all_rwds": np.vstack(rwds_list).reshape(length, -1),
"all_dones": np.vstack(dones_list).reshape(length, -1)
}
# with open('../saved/{}-trajectory.pkl'.format(str(self.__class__).split("'")[-2].split(".")[-1]), 'wb+') as f:
# pkl.dump(trajectory_dic, f, protocol=2)
return self, trajectory_dic
def setup_model(self):
with SetVerbosity(self.verbose):
self.graph = tf.Graph()
with self.graph.as_default():
self.set_random_seed(self.seed)
self.sess = tf_util.make_session(num_cpu=self.n_cpu_tf_sess,
graph=self.graph)
# Construct network for new policy
self.policy_pi = self.policy(self.sess,
self.observation_space,
self.action_space,
self.n_envs,
1,
None,
reuse=False,
**self.policy_kwargs)
# Network for old policy
with tf.variable_scope("oldpi", reuse=False):
old_pi = self.policy(self.sess,
self.observation_space,
self.action_space,
self.n_envs,
1,
None,
reuse=False,
**self.policy_kwargs)
with tf.variable_scope("loss", reuse=False):
self.grad_inverter = grad_inverter(
[self.action_space.high, self.action_space.low])
# Target advantage function (if applicable)
atarg = tf.placeholder(dtype=tf.float32, shape=[None])
# Empirical return
ret = tf.placeholder(dtype=tf.float32, shape=[None])
# learning rate multiplier, updated with schedule
lrmult = tf.placeholder(name='lrmult',
dtype=tf.float32,
shape=[])
# Annealed cliping parameter epislon
clip_param = self.clip_param * lrmult
obs_ph = self.policy_pi.obs_ph
action_ph = self.policy_pi.pdtype.sample_placeholder(
[None])
if debug:
action_ph_val = tf.Print(
action_ph, [
action_ph,
],
'\n\n ====================Unclipped action in: \n',
summarize=-1)
action_ph_val = tf.Print(
action_ph_val, [],
'\n ======================================== \n',
summarize=-1)
kloldnew = old_pi.proba_distribution.kl(
self.policy_pi.proba_distribution)
# old_logstd = old_pi.proba_distribution.logstd
# new_logstd = self.policy_pi.proba_distribution.logstd
# old_std = old_pi.proba_distribution.std
# new_std = self.policy_pi.proba_distribution.std
ent = self.policy_pi.proba_distribution.entropy()
meankl = tf.reduce_mean(kloldnew)
# meankl = tf.Print(meankl, [meankl,], "kl value: ")
# meankl_log = tf.Print(meankl, [old_logstd,], "high kl, old logstd value: ", summarize=-1)
# meankl_log = tf.Print(meankl_log, [new_logstd,], "high kl, new logstd value: ", summarize=-1)
# meankl_log = tf.Print(meankl_log, [old_std,], "high kl, old std value: ", summarize=-1)
# meankl_log = tf.Print(meankl_log, [new_std,], "high kl, new std value: ", summarize=-1)
# meanklvalue_ = tf.where(
# tf.greater(meankl, tf.constant(1, dtype = tf.float32)),
# meankl_log,
# meankl)
meanent = tf.reduce_mean(ent)
pol_entpen = (-self.entcoeff) * meanent
# pnew / pold
if debug:
old_logp = old_pi.proba_distribution.logp(
action_ph_val)
old_mean = old_pi.proba_distribution.mode()
old_std = old_pi.proba_distribution.std
old_logp = tf.Print(old_logp, [
old_logp,
],
'====== OLD logp: \n',
summarize=-1)
old_logp = tf.Print(old_logp, [], '\n', summarize=-1)
old_logp = tf.Print(old_logp, [
old_mean,
],
'====== OLD mean: \n',
summarize=-1)
old_logp = tf.Print(old_logp, [], '\n', summarize=-1)
old_logp = tf.Print(old_logp, [
old_std,
],
'====== OLD std: \n',
summarize=-1)
old_logp = tf.Print(old_logp, [], '\n', summarize=-1)
now_logp = self.policy_pi.proba_distribution.logp(
action_ph_val)
now_mean = self.policy_pi.proba_distribution.mode()
now_std = self.policy_pi.proba_distribution.std
now_logp = tf.Print(now_logp, [
now_logp,
],
'====== NOW logp: \n',
summarize=-1)
now_logp = tf.Print(now_logp, [], '\n', summarize=-1)
now_logp = tf.Print(now_logp, [
now_mean,
],
'====== NOW mean: \n',
summarize=-1)
now_logp = tf.Print(now_logp, [], '\n', summarize=-1)
now_logp = tf.Print(now_logp, [
now_std,
],
'====== NOW std: \n',
summarize=-1)
now_logp = tf.Print(now_logp, [], '\n', summarize=-1)
else:
now_logp = self.policy_pi.proba_distribution.logp(
action_ph)
old_logp = old_pi.proba_distribution.logp(action_ph)
ratio = tf.exp(now_logp - old_logp)
if debug:
ratio = tf.Print(ratio, [
ratio,
],
'ratio: \n',
summarize=-1)
ratio = tf.Print(ratio, [], '\n', summarize=-1)
# surrogate from conservative policy iteration
surr1 = ratio * atarg
surr2 = tf.clip_by_value(ratio, 1.0 - clip_param,
1.0 + clip_param) * atarg
# PPO's pessimistic surrogate (L^CLIP)
pol_surr = -tf.reduce_mean(tf.minimum(surr1, surr2))
vf_loss = tf.reduce_mean(
tf.square(self.policy_pi.value_flat - ret))
total_loss = pol_surr + pol_entpen + vf_loss
losses = [pol_surr, pol_entpen, vf_loss, meankl, meanent]
# losses = [pol_surr, pol_entpen, vf_loss, meanklvalue_, meanent]
self.loss_names = [
"pol_surr", "pol_entpen", "vf_loss", "kl", "ent"
]
tf.summary.scalar('entropy_loss', pol_entpen)
tf.summary.scalar('policy_gradient_loss', pol_surr)
tf.summary.scalar('value_function_loss', vf_loss)
tf.summary.scalar('approximate_kullback-leibler', meankl)
tf.summary.scalar('clip_factor', clip_param)
tf.summary.scalar('loss', total_loss)
self.params = tf_util.get_trainable_vars("model")
self.assign_old_eq_new = tf_util.function(
[], [],
updates=[
tf.assign(oldv, newv) for (oldv, newv) in zipsame(
tf_util.get_globals_vars("oldpi"),
tf_util.get_globals_vars("model"))
])
with tf.variable_scope("Adam_mpi", reuse=False):
self.adam = MpiAdam(self.params,
epsilon=self.adam_epsilon,
sess=self.sess)
with tf.variable_scope("input_info", reuse=False):
tf.summary.scalar('discounted_rewards',
tf.reduce_mean(ret))
tf.summary.scalar('learning_rate',
tf.reduce_mean(self.optim_stepsize))
tf.summary.scalar('advantage', tf.reduce_mean(atarg))
tf.summary.scalar('clip_range',
tf.reduce_mean(self.clip_param))
if self.full_tensorboard_log:
tf.summary.histogram('discounted_rewards', ret)
tf.summary.histogram('learning_rate',
self.optim_stepsize)
tf.summary.histogram('advantage', atarg)
tf.summary.histogram('clip_range', self.clip_param)
if tf_util.is_image(self.observation_space):
tf.summary.image('observation', obs_ph)
else:
tf.summary.histogram('observation', obs_ph)
self.step = self.policy_pi.step
self.proba_step = self.policy_pi.proba_step
self.initial_state = self.policy_pi.initial_state
tf_util.initialize(sess=self.sess)
self.summary = tf.summary.merge_all()
self.lossandgrad = tf_util.function(
[obs_ph, old_pi.obs_ph, action_ph, atarg, ret, lrmult],
[self.summary,
tf_util.flatgrad(total_loss, self.params)] + losses)
self.compute_losses = tf_util.function(
[obs_ph, old_pi.obs_ph, action_ph, atarg, ret, lrmult],
losses)
def setup_model(self):
with SetVerbosity(self.verbose):
self.graph = tf.Graph()
with self.graph.as_default():
self.sess = tf_util.single_threaded_session(graph=self.graph)
# Construct network for new policy
with tf.variable_scope("pi", reuse=False):
self.policy_pi = self.policy(self.sess,
self.observation_space,
self.action_space,
self.n_envs,
1,
None,
reuse=False)
# Network for old policy
with tf.variable_scope("oldpi", reuse=False):
old_pi = self.policy(self.sess,
self.observation_space,
self.action_space,
self.n_envs,
1,
None,
reuse=False)
# Target advantage function (if applicable)
atarg = tf.placeholder(dtype=tf.float32, shape=[None])
# Empirical return
ret = tf.placeholder(dtype=tf.float32, shape=[None])
# learning rate multiplier, updated with schedule
lrmult = tf.placeholder(name='lrmult',
dtype=tf.float32,
shape=[])
# Annealed cliping parameter epislon
clip_param = self.clip_param * lrmult
obs_ph = self.policy_pi.obs_ph
action_ph = self.policy_pi.pdtype.sample_placeholder([None])
kloldnew = old_pi.proba_distribution.kl(
self.policy_pi.proba_distribution)
ent = self.policy_pi.proba_distribution.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
pol_entpen = (-self.entcoeff) * meanent
# pnew / pold
ratio = tf.exp(
self.policy_pi.proba_distribution.logp(action_ph) -
old_pi.proba_distribution.logp(action_ph))
# surrogate from conservative policy iteration
surr1 = ratio * atarg
surr2 = tf.clip_by_value(ratio, 1.0 - clip_param,
1.0 + clip_param) * atarg
# PPO's pessimistic surrogate (L^CLIP)
pol_surr = -tf.reduce_mean(tf.minimum(surr1, surr2))
vf_loss = tf.reduce_mean(
tf.square(self.policy_pi.value_fn[:, 0] - ret))
total_loss = pol_surr + pol_entpen + vf_loss
losses = [pol_surr, pol_entpen, vf_loss, meankl, meanent]
self.loss_names = [
"pol_surr", "pol_entpen", "vf_loss", "kl", "ent"
]
self.params = tf_util.get_trainable_vars("pi")
self.lossandgrad = tf_util.function(
[obs_ph, old_pi.obs_ph, action_ph, atarg, ret, lrmult],
losses + [tf_util.flatgrad(total_loss, self.params)])
self.adam = MpiAdam(self.params,
epsilon=self.adam_epsilon,
sess=self.sess)
self.assign_old_eq_new = tf_util.function(
[], [],
updates=[
tf.assign(oldv, newv) for (
oldv,
newv) in zipsame(tf_util.get_globals_vars("oldpi"),
tf_util.get_globals_vars("pi"))
])
self.compute_losses = tf_util.function(
[obs_ph, old_pi.obs_ph, action_ph, atarg, ret, lrmult],
losses)
self.step = self.policy_pi.step
self.proba_step = self.policy_pi.proba_step
self.initial_state = self.policy_pi.initial_state
tf_util.initialize(sess=self.sess)
def learn(self,
total_timesteps,
callback=None,
seed=None,
log_interval=100):
with SetVerbosity(self.verbose):
self._setup_learn(seed)
with self.sess.as_default():
self.adam.sync()
# Prepare for rollouts
seg_gen = traj_segment_generator(self.policy_pi, self.env,
self.timesteps_per_actorbatch)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
t_start = time.time()
# rolling buffer for episode lengths
lenbuffer = deque(maxlen=100)
# rolling buffer for episode rewards
rewbuffer = deque(maxlen=100)
while True:
if callback:
callback(locals(), globals())
if total_timesteps and timesteps_so_far >= total_timesteps:
break
if self.schedule == 'constant':
cur_lrmult = 1.0
elif self.schedule == 'linear':
cur_lrmult = max(
1.0 - float(timesteps_so_far) / total_timesteps, 0)
else:
raise NotImplementedError
logger.log("********** Iteration %i ************" %
iters_so_far)
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))
obs_ph, action_ph, atarg, tdlamret = seg["ob"], seg[
"ac"], seg["adv"], seg["tdlamret"]
# predicted value function before udpate
vpredbefore = seg["vpred"]
# standardized advantage function estimate
atarg = (atarg - atarg.mean()) / atarg.std()
dataset = Dataset(
dict(ob=obs_ph,
ac=action_ph,
atarg=atarg,
vtarg=tdlamret),
shuffle=not issubclass(self.policy, LstmPolicy))
optim_batchsize = self.optim_batchsize or obs_ph.shape[0]
# set old parameter values to new parameter values
self.assign_old_eq_new(sess=self.sess)
logger.log("Optimizing...")
logger.log(fmt_row(13, self.loss_names))
# Here we do a bunch of optimization epochs over the data
for _ in range(self.optim_epochs):
# list of tuples, each of which gives the loss for a minibatch
losses = []
for batch in dataset.iterate_once(optim_batchsize):
*newlosses, grad = self.lossandgrad(batch["ob"],
batch["ob"],
batch["ac"],
batch["atarg"],
batch["vtarg"],
cur_lrmult,
sess=self.sess)
self.adam.update(grad,
self.optim_stepsize * cur_lrmult)
losses.append(newlosses)
logger.log(fmt_row(13, np.mean(losses, axis=0)))
logger.log("Evaluating losses...")
losses = []
for batch in dataset.iterate_once(optim_batchsize):
newlosses = self.compute_losses(batch["ob"],
batch["ob"],
batch["ac"],
batch["atarg"],
batch["vtarg"],
cur_lrmult,
sess=self.sess)
losses.append(newlosses)
mean_losses, _, _ = mpi_moments(losses, axis=0)
logger.log(fmt_row(13, mean_losses))
for (loss_val, name) in zipsame(mean_losses,
self.loss_names):
logger.record_tabular("loss_" + name, loss_val)
logger.record_tabular(
"ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
# local values
lrlocal = (seg["ep_lens"], seg["ep_rets"])
# list of tuples
listoflrpairs = MPI.COMM_WORLD.allgather(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 += seg["total_timestep"]
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 self.verbose >= 1 and MPI.COMM_WORLD.Get_rank() == 0:
logger.dump_tabular()
return self
def setup_model(self):
# prevent import loops
from stable_baselines.gail.adversary import TransitionClassifier
from stable_baselines.mdal.adversary import TabularAdversaryTF, NeuralAdversaryTRPO
with SetVerbosity(self.verbose):
assert issubclass(self.policy, ActorCriticPolicy), "Error: the input policy for the MDPO model must be " \
"an instance of common.policies.ActorCriticPolicy."
self.nworkers = MPI.COMM_WORLD.Get_size()
self.rank = MPI.COMM_WORLD.Get_rank()
np.set_printoptions(precision=3)
self.graph = tf.Graph()
with self.graph.as_default():
self.sess = tf_util.single_threaded_session(graph=self.graph)
# self._setup_learn(self.seed)
self._setup_learn()
if self.using_gail:
self.reward_giver = TransitionClassifier(self.observation_space, self.action_space,
self.hidden_size_adversary,
entcoeff=self.adversary_entcoeff)
elif self.using_mdal:
if self.neural:
self.reward_giver = NeuralAdversaryTRPO(self.sess, self.observation_space, self.action_space,
self.hidden_size_adversary,
entcoeff=self.adversary_entcoeff)
else:
self.reward_giver = TabularAdversaryTF(self.sess, self.observation_space, self.action_space,
self.hidden_size_adversary,
entcoeff=self.adversary_entcoeff,
expert_features=self.expert_dataset.successor_features,
exploration_bonus=self.exploration_bonus,
bonus_coef=self.bonus_coef,
t_c=self.t_c,
is_action_features=self.is_action_features)
# Construct network for new policy
self.policy_pi = self.policy(self.sess, self.observation_space, self.action_space, self.n_envs, 1,
None, reuse=False, **self.policy_kwargs)
# Network for old policy
with tf.variable_scope("oldpi", reuse=False):
self.old_policy = self.policy(self.sess, self.observation_space, self.action_space, self.n_envs, 1,
None, reuse=False, **self.policy_kwargs)
# Network for fitting closed form
with tf.variable_scope("closedpi", reuse=False):
self.closed_policy = self.policy(self.sess, self.observation_space, self.action_space, self.n_envs, 1,
None, reuse=False, **self.policy_kwargs)
with tf.variable_scope("loss", reuse=False):
self.atarg = tf.placeholder(dtype=tf.float32, shape=[None]) # Target advantage function (if applicable)
self.vtarg = tf.placeholder(dtype=tf.float32, shape=[None])
self.ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
self.learning_rate_ph = tf.placeholder(dtype=tf.float32, shape=[], name="learning_rate_ph")
self.outer_learning_rate_ph = tf.placeholder(dtype=tf.float32, shape=[], name="outer_learning_rate_ph")
self.old_vpred_ph = tf.placeholder(dtype=tf.float32, shape=[None], name="old_vpred_ph")
self.clip_range_vf_ph = tf.placeholder(dtype=tf.float32, shape=[], name="clip_range_ph")
observation = self.policy_pi.obs_ph
self.action = self.policy_pi.pdtype.sample_placeholder([None])
if self.tsallis_q == 1.0:
kloldnew = self.policy_pi.proba_distribution.kl(self.old_policy.proba_distribution)
ent = self.policy_pi.proba_distribution.entropy()
meankl = tf.reduce_mean(kloldnew)
else:
logp_pi = self.policy_pi.proba_distribution.logp(self.action)
logp_pi_old = self.old_policy.proba_distribution.logp(self.action)
ent = self.policy_pi.proba_distribution.entropy()
#kloldnew = self.policy_pi.proba_distribution.kl_tsallis(self.old_policy.proba_distribution, self.tsallis_q)
tsallis_q = 2.0 - self.tsallis_q
meankl = tf.reduce_mean(tf_log_q(tf.exp(logp_pi), tsallis_q) - tf_log_q(tf.exp(logp_pi_old), tsallis_q)) #tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
entbonus = self.entcoeff * meanent
if self.cliprange_vf is None:
vpred_clipped = self.policy_pi.value_flat
else:
vpred_clipped = self.old_vpred_ph + \
tf.clip_by_value(self.policy_pi.value_flat - self.old_vpred_ph,
- self.clip_range_vf_ph, self.clip_range_vf_ph)
vf_losses1 = tf.square(self.policy_pi.value_flat - self.ret)
vf_losses2 = tf.square(vpred_clipped - self.ret)
vferr = tf.reduce_mean(tf.maximum(vf_losses1, vf_losses2))
# advantage * pnew / pold
ratio = tf.exp(self.policy_pi.proba_distribution.logp(self.action) -
self.old_policy.proba_distribution.logp(self.action))
if self.method == "multistep-SGD":
surrgain = tf.reduce_mean(ratio * self.atarg) - meankl / self.learning_rate_ph
elif self.method == "closedreverse-KL":
surrgain = tf.reduce_mean(tf.exp(self.atarg) * self.policy_pi.proba_distribution.logp(self.action))
else:
policygain = tf.reduce_mean(tf.exp(self.atarg) * tf.log(self.closed_policy.proba_distribution.mean))
surrgain = tf.reduce_mean(ratio * self.atarg) - tf.reduce_mean(self.learning_rate_ph * ratio * self.policy_pi.proba_distribution.logp(self.action))
optimgain = surrgain #+ entbonus - self.learning_rate_ph * meankl
losses = [optimgain, meankl, entbonus, surrgain, meanent]
self.loss_names = ["optimgain", "meankl", "entloss", "surrgain", "entropy"]
dist = meankl
all_var_list = tf_util.get_trainable_vars("model")
var_list = [v for v in all_var_list if "/vf" not in v.name and "/q/" not in v.name]
vf_var_list = [v for v in all_var_list if "/pi" not in v.name and "/logstd" not in v.name]
print("policy vars", var_list)
all_closed_var_list = tf_util.get_trainable_vars("closedpi")
closed_var_list = [v for v in all_closed_var_list if "/vf" not in v.name and "/q" not in v.name]
self.get_flat = tf_util.GetFlat(var_list, sess=self.sess)
self.set_from_flat = tf_util.SetFromFlat(var_list, sess=self.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)
# tf.summary.scalar('entropy_loss', meanent)
# tf.summary.scalar('policy_gradient_loss', optimgain)
# tf.summary.scalar('value_function_loss', surrgain)
# tf.summary.scalar('approximate_kullback-leibler', meankl)
# tf.summary.scalar('loss', optimgain + meankl + entbonus + surrgain + meanent)
self.assign_old_eq_new = \
tf_util.function([], [], updates=[tf.assign(oldv, newv) for (oldv, newv) in
zipsame(tf_util.get_globals_vars("oldpi"),
tf_util.get_globals_vars("model"))])
self.compute_losses = tf_util.function([observation, self.old_policy.obs_ph, self.action, self.atarg, self.learning_rate_ph, self.vtarg], losses)
self.compute_fvp = tf_util.function([flat_tangent, observation, self.old_policy.obs_ph, self.action, self.atarg],
fvp)
self.compute_vflossandgrad = tf_util.function([observation, self.old_policy.obs_ph, self.ret, self.old_vpred_ph, self.clip_range_vf_ph],
tf_util.flatgrad(vferr, vf_var_list))
grads = tf.gradients(-optimgain, var_list)
grads, _grad_norm = tf.clip_by_global_norm(grads, 0.5)
trainer = tf.train.AdamOptimizer(learning_rate=self.outer_learning_rate_ph, epsilon=1e-5)
# trainer = tf.train.AdamOptimizer(learning_rate=3e-4, epsilon=1e-5)
grads = list(zip(grads, var_list))
self._train = trainer.apply_gradients(grads)
@contextmanager
def timed(msg):
if self.rank == 0 and self.verbose >= 1:
# print(colorize(msg, color='magenta'))
# start_time = time.time()
yield
# print(colorize("done in {:.3f} seconds".format((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 /= self.nworkers
return out
tf_util.initialize(sess=self.sess)
th_init = self.get_flat()
MPI.COMM_WORLD.Bcast(th_init, root=0)
self.set_from_flat(th_init)
with tf.variable_scope("Adam_mpi", reuse=False):
self.vfadam = MpiAdam(vf_var_list, sess=self.sess)
if self.using_gail or self.using_mdal:
self.d_adam = MpiAdam(self.reward_giver.get_trainable_variables(), sess=self.sess)
self.d_adam.sync()
self.vfadam.sync()
with tf.variable_scope("input_info", reuse=False):
tf.summary.scalar('discounted_rewards', tf.reduce_mean(self.ret))
tf.summary.scalar('learning_rate', tf.reduce_mean(self.vf_stepsize))
tf.summary.scalar('advantage', tf.reduce_mean(self.atarg))
tf.summary.scalar('kl_clip_range', tf.reduce_mean(self.max_kl))
if self.full_tensorboard_log:
tf.summary.histogram('discounted_rewards', self.ret)
tf.summary.histogram('learning_rate', self.vf_stepsize)
tf.summary.histogram('advantage', self.atarg)
tf.summary.histogram('kl_clip_range', self.max_kl)
if tf_util.is_image(self.observation_space):
tf.summary.image('observation', observation)
else:
tf.summary.histogram('observation', observation)
self.timed = timed
self.allmean = allmean
self.step = self.policy_pi.step
self.proba_step = self.policy_pi.proba_step
self.initial_state = self.policy_pi.initial_state
self.params = tf_util.get_trainable_vars("model") + tf_util.get_trainable_vars("oldpi")
if self.using_gail:
self.params.extend(self.reward_giver.get_trainable_variables())
self.summary = tf.summary.merge_all()
self.compute_lossandgrad = \
tf_util.function([observation, self.old_policy.obs_ph, self.action, self.atarg, self.ret, self.learning_rate_ph, self.vtarg, self.closed_policy.obs_ph],
[self.summary, tf_util.flatgrad(optimgain, var_list)] + losses)
def general_actor_critic(input_shape_vec,
act_output_shape,
comm,
learn_rate=[0.001, 0.001],
trainable=True,
label=""):
sess = K.get_session()
np.random.seed(0)
tf.set_random_seed(0)
# network 1 (new policy)
with tf.variable_scope(label + "_pi_new", reuse=False):
inp = Input(shape=input_shape_vec) # [5,6,3]
# rc_lyr = Lambda(lambda x: ned_to_ripCoords_tf(x, 4000))(inp)
trunk_x = Reshape([input_shape_vec[0], input_shape_vec[1] * 3])(inp)
trunk_x = LSTM(128)(trunk_x)
dist, sample_action_op, action_ph, value_output = ppo_continuous(
3, trunk_x)
# network 2 (old policy)
with tf.variable_scope(label + "_pi_old", reuse=False):
inp_old = Input(shape=input_shape_vec) # [5,6,3]
# rc_lyr = Lambda(lambda x: ned_to_ripCoords_tf(x, 4000))(inp_old)
trunk_x = Reshape([input_shape_vec[0],
input_shape_vec[1] * 3])(inp_old)
trunk_x = LSTM(128)(trunk_x)
dist_old, sample_action_op_old, action_ph_old, value_output_old = ppo_continuous(
3, trunk_x)
# additional placeholders
adv_ph = tf.placeholder(tf.float32, [None], name="advantages_ph")
alpha_ph = tf.placeholder(tf.float32, shape=(), name="alpha_ph")
vtarg = tf.placeholder(tf.float32, [None]) # target value placeholder
# loss
loss = ppo_continuous_loss(dist, dist_old, value_output, action_ph,
alpha_ph, adv_ph, vtarg)
# gradient
with tf.variable_scope("grad", reuse=False):
gradient = tf_util.flatgrad(
loss, tf_util.get_trainable_vars(label + "_pi_new"))
adam = MpiAdam(tf_util.get_trainable_vars(label + "_pi_new"),
epsilon=0.00001,
sess=sess,
comm=comm)
# method for sync'ing the two policies
assign_old_eq_new = tf_util.function(
[], [],
updates=[
tf.assign(oldv, newv)
for (oldv,
newv) in zipsame(tf_util.get_globals_vars(label + "_pi_old"),
tf_util.get_globals_vars(label + "_pi_new"))
])
# initialize all the things
init_op = tf.global_variables_initializer()
sess.run(init_op)
# methods for interacting with this model
def sync_weights():
assign_old_eq_new(sess=sess)
def sample_action(states, logstd_override=None):
a = sess.run(sample_action_op, feed_dict={inp: states})
return a
def sample_value(states):
v = sess.run(value_output, feed_dict={inp: states})
return v
def train(states, actions, vtarget, advs, alpha):
alpha = max(alpha, 0.0)
adam_lr = learn_rate[0]
g = sess.run(
[gradient],
feed_dict={
inp: states,
inp_old: states,
action_ph: actions,
adv_ph: advs,
alpha_ph: alpha,
vtarg: vtarget
})
adam.update(g[0], adam_lr * alpha)
# initial sync
adam.sync()
sync_weights()
return sync_weights, sample_action, sample_value, train