def _build_inference_loss(self, i):
""" Build loss function for the inference network """
infer_dist = self.inference._dist
with tf.name_scope("infer_loss"):
traj_ll_flat = self.inference.log_likelihood_sym(
i.flat.trajectory_var, i.flat.latent_var, name="traj_ll_flat")
traj_ll = tf.reshape(traj_ll_flat, [-1, self.max_path_length],
name="traj_ll")
# Calculate loss
traj_gammas = tf.constant(float(self.discount),
dtype=tf.float32,
shape=[self.max_path_length])
traj_discounts = tf.cumprod(traj_gammas,
exclusive=True,
name="traj_discounts")
discount_traj_ll = traj_discounts * traj_ll
discount_traj_ll_flat = flatten_batch(discount_traj_ll,
name="discount_traj_ll_flat")
discount_traj_ll_valid = filter_valids(
discount_traj_ll_flat,
i.flat.valid_var,
name="discount_traj_ll_valid")
with tf.name_scope("loss"):
infer_loss = -tf.reduce_mean(discount_traj_ll_valid,
name="infer_loss")
with tf.name_scope("kl"):
# Calculate predicted embedding distributions for each timestep
infer_dist_info_flat = self.inference.dist_info_sym(
i.flat.trajectory_var,
i.flat.infer_state_info_vars,
name="infer_dist_info_flat")
infer_dist_info_valid = filter_valids_dict(
infer_dist_info_flat,
i.flat.valid_var,
name="infer_dist_info_valid")
# Calculate KL divergence
kl = infer_dist.kl_sym(i.valid.infer_old_dist_info_vars,
infer_dist_info_valid)
infer_kl = tf.reduce_mean(kl, name="infer_kl")
return infer_loss, infer_kl
def _build_policy_loss(self, i):
""" Build policy network loss """
pol_dist = self.policy._dist
# Entropy terms
embedding_entropy, inference_ce, policy_entropy = \
self._build_entropy_terms(i)
# Augment the path rewards with entropy terms
with tf.name_scope("augmented_rewards"):
rewards = i.reward_var \
- (self.inference_ce_coeff * inference_ce) \
+ (self.policy_ent_coeff * policy_entropy)
with tf.name_scope("policy_loss"):
with tf.name_scope("advantages"):
advantages = compute_advantages(self.discount,
self.gae_lambda,
self.max_path_length,
i.baseline_var,
rewards,
name="advantages")
# Flatten and filter valids
adv_flat = flatten_batch(advantages, name="adv_flat")
adv_valid = filter_valids(adv_flat,
i.flat.valid_var,
name="adv_valid")
policy_dist_info_flat = self.policy.dist_info_sym(
i.flat.task_var,
i.flat.obs_var,
i.flat.policy_state_info_vars,
name="policy_dist_info_flat")
policy_dist_info_valid = filter_valids_dict(
policy_dist_info_flat,
i.flat.valid_var,
name="policy_dist_info_valid")
# Optionally normalize advantages
eps = tf.constant(1e-8, dtype=tf.float32)
if self.center_adv:
with tf.name_scope("center_adv"):
mean, var = tf.nn.moments(adv_valid, axes=[0])
adv_valid = tf.nn.batch_normalization(
adv_valid, mean, var, 0, 1, eps)
if self.positive_adv:
with tf.name_scope("positive_adv"):
m = tf.reduce_min(adv_valid)
adv_valid = (adv_valid - m) + eps
# Calculate loss function and KL divergence
with tf.name_scope("kl"):
kl = pol_dist.kl_sym(
i.valid.policy_old_dist_info_vars,
policy_dist_info_valid,
)
pol_mean_kl = tf.reduce_mean(kl)
# Calculate surrogate loss
with tf.name_scope("surr_loss"):
lr = pol_dist.likelihood_ratio_sym(
i.valid.action_var,
i.valid.policy_old_dist_info_vars,
policy_dist_info_valid,
name="lr")
# Policy gradient surrogate objective
surr_vanilla = lr * adv_valid
if self._pg_loss == PGLoss.VANILLA:
# VPG, TRPO use the standard surrogate objective
surr_obj = tf.identity(surr_vanilla, name="surr_obj")
elif self._pg_loss == PGLoss.CLIP:
# PPO uses a surrogate objective with clipped LR
lr_clip = tf.clip_by_value(lr,
1 - self.lr_clip_range,
1 + self.lr_clip_range,
name="lr_clip")
surr_clip = lr_clip * adv_valid
surr_obj = tf.minimum(surr_vanilla,
surr_clip,
name="surr_obj")
else:
raise NotImplementedError("Unknown PGLoss")
# Maximize E[surrogate objective] by minimizing
# -E_t[surrogate objective]
surr_loss = -tf.reduce_mean(surr_obj)
# Embedding entropy bonus
surr_loss -= self.embedding_ent_coeff * embedding_entropy
embed_mean_kl = self._build_embedding_kl(i)
# Diagnostic functions
self.f_policy_kl = tensor_utils.compile_function(
flatten_inputs(self._policy_opt_inputs),
pol_mean_kl,
log_name="f_policy_kl")
self.f_rewards = tensor_utils.compile_function(flatten_inputs(
self._policy_opt_inputs),
rewards,
log_name="f_rewards")
# returns = self._build_returns(rewards)
returns = discounted_returns(self.discount,
self.max_path_length,
rewards,
name="returns")
self.f_returns = tensor_utils.compile_function(flatten_inputs(
self._policy_opt_inputs),
returns,
log_name="f_returns")
return surr_loss, pol_mean_kl, embed_mean_kl
def _build_policy_loss(self, i):
pol_dist = self.policy.distribution
policy_entropy = self._build_entropy_term(i)
with tf.name_scope("augmented_rewards"):
rewards = i.reward_var + (self.policy_ent_coeff * policy_entropy)
with tf.name_scope("policy_loss"):
advantages = compute_advantages(
self.discount,
self.gae_lambda,
self.max_path_length,
i.baseline_var,
rewards,
name="advantages")
adv_flat = flatten_batch(advantages, name="adv_flat")
adv_valid = filter_valids(
adv_flat, i.flat.valid_var, name="adv_valid")
if self.policy.recurrent:
advantages = tf.reshape(advantages, [-1, self.max_path_length])
# Optionally normalize advantages
eps = tf.constant(1e-8, dtype=tf.float32)
if self.center_adv:
with tf.name_scope("center_adv"):
mean, var = tf.nn.moments(adv_valid, axes=[0])
adv_valid = tf.nn.batch_normalization(
adv_valid, mean, var, 0, 1, eps)
if self.positive_adv:
with tf.name_scope("positive_adv"):
m = tf.reduce_min(adv_valid)
adv_valid = (adv_valid - m) + eps
if self.policy.recurrent:
policy_dist_info = self.policy.dist_info_sym(
i.obs_var,
i.policy_state_info_vars,
name="policy_dist_info")
else:
policy_dist_info_flat = self.policy.dist_info_sym(
i.flat.obs_var,
i.flat.policy_state_info_vars,
name="policy_dist_info_flat")
policy_dist_info_valid = filter_valids_dict(
policy_dist_info_flat,
i.flat.valid_var,
name="policy_dist_info_valid")
# Calculate loss function and KL divergence
with tf.name_scope("kl"):
if self.policy.recurrent:
kl = pol_dist.kl_sym(
i.policy_old_dist_info_vars,
policy_dist_info,
)
pol_mean_kl = tf.reduce_sum(
kl * i.valid_var) / tf.reduce_sum(i.valid_var)
else:
kl = pol_dist.kl_sym(
i.valid.policy_old_dist_info_vars,
policy_dist_info_valid,
)
pol_mean_kl = tf.reduce_mean(kl)
# Calculate vanilla loss
with tf.name_scope("vanilla_loss"):
if self.policy.recurrent:
ll = pol_dist.log_likelihood_sym(
i.action_var, policy_dist_info, name="log_likelihood")
vanilla = ll * advantages * i.valid_var
else:
ll = pol_dist.log_likelihood_sym(
i.valid.action_var,
policy_dist_info_valid,
name="log_likelihood")
vanilla = ll * adv_valid
# Calculate surrogate loss
with tf.name_scope("surrogate_loss"):
if self.policy.recurrent:
lr = pol_dist.likelihood_ratio_sym(
i.action_var,
i.policy_old_dist_info_vars,
policy_dist_info,
name="lr")
surrogate = lr * advantages * i.valid_var
else:
lr = pol_dist.likelihood_ratio_sym(
i.valid.action_var,
i.valid.policy_old_dist_info_vars,
policy_dist_info_valid,
name="lr")
surrogate = lr * adv_valid
# Finalize objective function
with tf.name_scope("loss"):
if self._pg_loss == PGLoss.VANILLA:
# VPG uses the vanilla objective
obj = tf.identity(vanilla, name="vanilla_obj")
elif self._pg_loss == PGLoss.SURROGATE:
# TRPO uses the standard surrogate objective
obj = tf.identity(surrogate, name="surr_obj")
elif self._pg_loss == PGLoss.SURROGATE_CLIP:
lr_clip = tf.clip_by_value(
lr,
1 - self.lr_clip_range,
1 + self.lr_clip_range,
name="lr_clip")
if self.policy.recurrent:
surr_clip = lr_clip * advantages * i.valid_var
else:
surr_clip = lr_clip * adv_valid
obj = tf.minimum(surrogate, surr_clip, name="surr_obj")
else:
raise NotImplementedError("Unknown PGLoss")
# Maximize E[surrogate objective] by minimizing
# -E_t[surrogate objective]
if self.policy.recurrent:
loss = -tf.reduce_sum(obj) / tf.reduce_sum(i.valid_var)
else:
loss = -tf.reduce_mean(obj)
# Diagnostic functions
self.f_policy_kl = tensor_utils.compile_function(
flatten_inputs(self._policy_opt_inputs),
pol_mean_kl,
log_name="f_policy_kl")
self.f_rewards = tensor_utils.compile_function(
flatten_inputs(self._policy_opt_inputs),
rewards,
log_name="f_rewards")
returns = discounted_returns(self.discount, self.max_path_length,
rewards)
self.f_returns = tensor_utils.compile_function(
flatten_inputs(self._policy_opt_inputs),
returns,
log_name="f_returns")
return loss, pol_mean_kl
def _build_inputs(self):
"""
Builds input variables (and trivial views thereof) for the loss
function network
"""
observation_space = self.policy.observation_space
action_space = self.policy.action_space
task_space = self.policy.task_space
latent_space = self.policy.latent_space
trajectory_space = self.inference.input_space
policy_dist = self.policy._dist
embed_dist = self.policy.embedding._dist
infer_dist = self.inference._dist
with tf.name_scope("inputs"):
obs_var = observation_space.new_tensor_variable(
'obs',
extra_dims=1 + 1,
)
task_var = task_space.new_tensor_variable(
'task',
extra_dims=1 + 1,
)
action_var = action_space.new_tensor_variable(
'action',
extra_dims=1 + 1,
)
reward_var = tensor_utils.new_tensor(
'reward',
ndim=1 + 1,
dtype=tf.float32,
)
latent_var = latent_space.new_tensor_variable(
'latent',
extra_dims=1 + 1,
)
baseline_var = tensor_utils.new_tensor(
'baseline',
ndim=1 + 1,
dtype=tf.float32,
)
trajectory_var = trajectory_space.new_tensor_variable(
'trajectory',
extra_dims=1 + 1,
)
valid_var = tf.placeholder(tf.float32,
shape=[None, None],
name="valid")
# Policy state (for RNNs)
policy_state_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + 1) + list(shape),
name=k)
for k, shape in self.policy.state_info_specs
}
policy_state_info_vars_list = [
policy_state_info_vars[k] for k in self.policy.state_info_keys
]
# Old policy distribution (for KL)
policy_old_dist_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + 1) + list(shape),
name='policy_old_%s' % k)
for k, shape in policy_dist.dist_info_specs
}
policy_old_dist_info_vars_list = [
policy_old_dist_info_vars[k]
for k in policy_dist.dist_info_keys
]
# Embedding state (for RNNs)
embed_state_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + 1) + list(shape),
name='embed_%s' % k)
for k, shape in self.policy.embedding.state_info_specs
}
embed_state_info_vars_list = [
embed_state_info_vars[k]
for k in self.policy.embedding.state_info_keys
]
# Old embedding distribution (for KL)
embed_old_dist_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + 1) + list(shape),
name='embed_old_%s' % k)
for k, shape in embed_dist.dist_info_specs
}
embed_old_dist_info_vars_list = [
embed_old_dist_info_vars[k] for k in embed_dist.dist_info_keys
]
# Inference distribution state (for RNNs)
infer_state_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + 1) + list(shape),
name='infer_%s' % k)
for k, shape in self.inference.state_info_specs
}
infer_state_info_vars_list = [
infer_state_info_vars[k]
for k in self.inference.state_info_keys
]
# Old inference distribution (for KL)
infer_old_dist_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + 1) + list(shape),
name='infer_old_%s' % k)
for k, shape in infer_dist.dist_info_specs
}
infer_old_dist_info_vars_list = [
infer_old_dist_info_vars[k] for k in infer_dist.dist_info_keys
]
# Flattened view
with tf.name_scope("flat"):
obs_flat = flatten_batch(obs_var, name="obs_flat")
task_flat = flatten_batch(task_var, name="task_flat")
action_flat = flatten_batch(action_var, name="action_flat")
reward_flat = flatten_batch(reward_var, name="reward_flat")
latent_flat = flatten_batch(latent_var, name="latent_flat")
trajectory_flat = flatten_batch(trajectory_var,
name="trajectory_flat")
valid_flat = flatten_batch(valid_var, name="valid_flat")
policy_state_info_vars_flat = flatten_batch_dict(
policy_state_info_vars, name="policy_state_info_vars_flat")
policy_old_dist_info_vars_flat = flatten_batch_dict(
policy_old_dist_info_vars,
name="policy_old_dist_info_vars_flat")
embed_state_info_vars_flat = flatten_batch_dict(
embed_state_info_vars, name="embed_state_info_vars_flat")
embed_old_dist_info_vars_flat = flatten_batch_dict(
embed_old_dist_info_vars,
name="embed_old_dist_info_vars_flat")
infer_state_info_vars_flat = flatten_batch_dict(
infer_state_info_vars, name="infer_state_info_vars_flat")
infer_old_dist_info_vars_flat = flatten_batch_dict(
infer_old_dist_info_vars,
name="infer_old_dist_info_vars_flat")
# Valid view
with tf.name_scope("valid"):
action_valid = filter_valids(action_flat,
valid_flat,
name="action_valid")
policy_state_info_vars_valid = filter_valids_dict(
policy_state_info_vars_flat,
valid_flat,
name="policy_state_info_vars_valid")
policy_old_dist_info_vars_valid = filter_valids_dict(
policy_old_dist_info_vars_flat,
valid_flat,
name="policy_old_dist_info_vars_valid")
embed_old_dist_info_vars_valid = filter_valids_dict(
embed_old_dist_info_vars_flat,
valid_flat,
name="embed_old_dist_info_vars_valid")
infer_old_dist_info_vars_valid = filter_valids_dict(
infer_old_dist_info_vars_flat,
valid_flat,
name="infer_old_dist_info_vars_valid")
# Policy and embedding network loss and optimizer inputs
pol_flat = graph_inputs(
"PolicyLossInputsFlat",
obs_var=obs_flat,
task_var=task_flat,
action_var=action_flat,
reward_var=reward_flat,
latent_var=latent_flat,
trajectory_var=trajectory_flat,
valid_var=valid_flat,
policy_state_info_vars=policy_state_info_vars_flat,
policy_old_dist_info_vars=policy_old_dist_info_vars_flat,
embed_state_info_vars=embed_state_info_vars_flat,
embed_old_dist_info_vars=embed_old_dist_info_vars_flat,
)
pol_valid = graph_inputs(
"PolicyLossInputsValid",
action_var=action_valid,
policy_state_info_vars=policy_state_info_vars_valid,
policy_old_dist_info_vars=policy_old_dist_info_vars_valid,
embed_old_dist_info_vars=embed_old_dist_info_vars_valid,
)
policy_loss_inputs = graph_inputs(
"PolicyLossInputs",
obs_var=obs_var,
action_var=action_var,
reward_var=reward_var,
baseline_var=baseline_var,
trajectory_var=trajectory_var,
task_var=task_var,
latent_var=latent_var,
valid_var=valid_var,
policy_state_info_vars=policy_state_info_vars,
policy_old_dist_info_vars=policy_old_dist_info_vars,
embed_state_info_vars=embed_state_info_vars,
embed_old_dist_info_vars=embed_old_dist_info_vars,
flat=pol_flat,
valid=pol_valid,
)
# Special variant for the optimizer
# * Uses lists instead of dicts for the distribution parameters
# * Omits flats and valids
# TODO: eliminate
policy_opt_inputs = graph_inputs(
"PolicyOptInputs",
obs_var=obs_var,
action_var=action_var,
reward_var=reward_var,
baseline_var=baseline_var,
trajectory_var=trajectory_var,
task_var=task_var,
latent_var=latent_var,
valid_var=valid_var,
policy_state_info_vars_list=policy_state_info_vars_list,
policy_old_dist_info_vars_list=policy_old_dist_info_vars_list,
embed_state_info_vars_list=embed_state_info_vars_list,
embed_old_dist_info_vars_list=embed_old_dist_info_vars_list,
)
# Inference network loss and optimizer inputs
infer_flat = graph_inputs(
"InferenceLossInputsFlat",
latent_var=latent_flat,
trajectory_var=trajectory_flat,
valid_var=valid_flat,
infer_state_info_vars=infer_state_info_vars_flat,
infer_old_dist_info_vars=infer_old_dist_info_vars_flat,
)
infer_valid = graph_inputs(
"InferenceLossInputsValid",
infer_old_dist_info_vars=infer_old_dist_info_vars_valid,
)
inference_loss_inputs = graph_inputs(
"InferenceLossInputs",
latent_var=latent_var,
trajectory_var=trajectory_var,
valid_var=valid_var,
infer_state_info_vars=infer_state_info_vars,
infer_old_dist_info_vars=infer_old_dist_info_vars,
flat=infer_flat,
valid=infer_valid,
)
# Special variant for the optimizer
# * Uses lists instead of dicts for the distribution parameters
# * Omits flats and valids
# TODO: eliminate
inference_opt_inputs = graph_inputs(
"InferenceOptInputs",
latent_var=latent_var,
trajectory_var=trajectory_var,
valid_var=valid_var,
infer_state_info_vars_list=infer_state_info_vars_list,
infer_old_dist_info_vars_list=infer_old_dist_info_vars_list,
)
return (policy_loss_inputs, policy_opt_inputs, inference_loss_inputs,
inference_opt_inputs)
def _build_inputs(self):
"""Decalre graph inputs variables."""
observation_space = self.policy.observation_space
action_space = self.policy.action_space
policy_dist = self.policy.distribution
with tf.name_scope('inputs'):
obs_var = observation_space.to_tf_placeholder(
name='obs',
batch_dims=2) # yapf: disable
action_var = action_space.to_tf_placeholder(
name='action',
batch_dims=2) # yapf: disable
reward_var = tensor_utils.new_tensor(
name='reward',
ndim=2,
dtype=tf.float32) # yapf: disable
valid_var = tensor_utils.new_tensor(
name='valid',
ndim=2,
dtype=tf.float32) # yapf: disable
feat_diff = tensor_utils.new_tensor(
name='feat_diff',
ndim=2,
dtype=tf.float32) # yapf: disable
param_v = tensor_utils.new_tensor(
name='param_v',
ndim=1,
dtype=tf.float32) # yapf: disable
param_eta = tensor_utils.new_tensor(
name='param_eta',
ndim=0,
dtype=tf.float32) # yapf: disable
policy_state_info_vars = {
k: tf.placeholder(
tf.float32,
shape=[None] * 2 + list(shape),
name=k)
for k, shape in self.policy.state_info_specs
} # yapf: disable
policy_state_info_vars_list = [
policy_state_info_vars[k]
for k in self.policy.state_info_keys
] # yapf: disable
policy_old_dist_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * 2 + list(shape),
name='policy_old_%s' % k)
for k, shape in policy_dist.dist_info_specs
}
policy_old_dist_info_vars_list = [
policy_old_dist_info_vars[k]
for k in policy_dist.dist_info_keys
]
with tf.name_scope('flat'):
obs_flat = flatten_batch(obs_var, name='obs_flat')
action_flat = flatten_batch(action_var, name='action_flat')
reward_flat = flatten_batch(reward_var, name='reward_flat')
valid_flat = flatten_batch(valid_var, name='valid_flat')
feat_diff_flat = flatten_batch(
feat_diff,
name='feat_diff_flat') # yapf: disable
policy_state_info_vars_flat = flatten_batch_dict(
policy_state_info_vars,
name='policy_state_info_vars_flat') # yapf: disable
policy_old_dist_info_vars_flat = flatten_batch_dict(
policy_old_dist_info_vars,
name='policy_old_dist_info_vars_flat')
with tf.name_scope('valid'):
reward_valid = filter_valids(
reward_flat,
valid_flat,
name='reward_valid') # yapf: disable
action_valid = filter_valids(
action_flat,
valid_flat,
name='action_valid') # yapf: disable
policy_state_info_vars_valid = filter_valids_dict(
policy_state_info_vars_flat,
valid_flat,
name='policy_state_info_vars_valid')
policy_old_dist_info_vars_valid = filter_valids_dict(
policy_old_dist_info_vars_flat,
valid_flat,
name='policy_old_dist_info_vars_valid')
pol_flat = graph_inputs(
'PolicyLossInputsFlat',
obs_var=obs_flat,
action_var=action_flat,
reward_var=reward_flat,
valid_var=valid_flat,
feat_diff=feat_diff_flat,
policy_state_info_vars=policy_state_info_vars_flat,
policy_old_dist_info_vars=policy_old_dist_info_vars_flat,
)
pol_valid = graph_inputs(
'PolicyLossInputsValid',
reward_var=reward_valid,
action_var=action_valid,
policy_state_info_vars=policy_state_info_vars_valid,
policy_old_dist_info_vars=policy_old_dist_info_vars_valid,
)
policy_loss_inputs = graph_inputs(
'PolicyLossInputs',
obs_var=obs_var,
action_var=action_var,
reward_var=reward_var,
valid_var=valid_var,
feat_diff=feat_diff,
param_eta=param_eta,
param_v=param_v,
policy_state_info_vars=policy_state_info_vars,
policy_old_dist_info_vars=policy_old_dist_info_vars,
flat=pol_flat,
valid=pol_valid,
)
policy_opt_inputs = graph_inputs(
'PolicyOptInputs',
obs_var=obs_var,
action_var=action_var,
reward_var=reward_var,
valid_var=valid_var,
feat_diff=feat_diff,
param_eta=param_eta,
param_v=param_v,
policy_state_info_vars_list=policy_state_info_vars_list,
policy_old_dist_info_vars_list=policy_old_dist_info_vars_list,
)
dual_opt_inputs = graph_inputs(
'DualOptInputs',
reward_var=reward_var,
valid_var=valid_var,
feat_diff=feat_diff,
param_eta=param_eta,
param_v=param_v,
policy_state_info_vars_list=policy_state_info_vars_list,
policy_old_dist_info_vars_list=policy_old_dist_info_vars_list,
)
return policy_loss_inputs, policy_opt_inputs, dual_opt_inputs
def _build_inputs(self):
observation_space = self.policy.observation_space
action_space = self.policy.action_space
policy_dist = self.policy.distribution
with tf.name_scope("inputs"):
obs_var = observation_space.new_tensor_variable(
name="obs", extra_dims=2)
action_var = action_space.new_tensor_variable(
name="action", extra_dims=2)
reward_var = tensor_utils.new_tensor(
name="reward", ndim=2, dtype=tf.float32)
valid_var = tf.placeholder(
tf.float32, shape=[None, None], name="valid")
baseline_var = tensor_utils.new_tensor(
name="baseline", ndim=2, dtype=tf.float32)
policy_state_info_vars = {
k: tf.placeholder(
tf.float32, shape=[None] * 2 + list(shape), name=k)
for k, shape in self.policy.state_info_specs
}
policy_state_info_vars_list = [
policy_state_info_vars[k] for k in self.policy.state_info_keys
]
# old policy distribution
policy_old_dist_info_vars = {
k: tf.placeholder(
tf.float32,
shape=[None] * 2 + list(shape),
name="policy_old_%s" % k)
for k, shape in policy_dist.dist_info_specs
}
policy_old_dist_info_vars_list = [
policy_old_dist_info_vars[k]
for k in policy_dist.dist_info_keys
]
# flattened view
with tf.name_scope("flat"):
obs_flat = flatten_batch(obs_var, name="obs_flat")
action_flat = flatten_batch(action_var, name="action_flat")
reward_flat = flatten_batch(reward_var, name="reward_flat")
valid_flat = flatten_batch(valid_var, name="valid_flat")
policy_state_info_vars_flat = flatten_batch_dict(
policy_state_info_vars, name="policy_state_info_vars_flat")
policy_old_dist_info_vars_flat = flatten_batch_dict(
policy_old_dist_info_vars,
name="policy_old_dist_info_vars_flat")
# valid view
with tf.name_scope("valid"):
action_valid = filter_valids(
action_flat, valid_flat, name="action_valid")
policy_state_info_vars_valid = filter_valids_dict(
policy_state_info_vars_flat,
valid_flat,
name="policy_state_info_vars_valid")
policy_old_dist_info_vars_valid = filter_valids_dict(
policy_old_dist_info_vars_flat,
valid_flat,
name="policy_old_dist_info_vars_valid")
# policy loss and optimizer inputs
pol_flat = graph_inputs(
"PolicyLossInputsFlat",
obs_var=obs_flat,
action_var=action_flat,
reward_var=reward_flat,
valid_var=valid_flat,
policy_state_info_vars=policy_state_info_vars_flat,
policy_old_dist_info_vars=policy_old_dist_info_vars_flat,
)
pol_valid = graph_inputs(
"PolicyLossInputsValid",
action_var=action_valid,
policy_state_info_vars=policy_state_info_vars_valid,
policy_old_dist_info_vars=policy_old_dist_info_vars_valid,
)
policy_loss_inputs = graph_inputs(
"PolicyLossInputs",
obs_var=obs_var,
action_var=action_var,
reward_var=reward_var,
baseline_var=baseline_var,
valid_var=valid_var,
policy_state_info_vars=policy_state_info_vars,
policy_old_dist_info_vars=policy_old_dist_info_vars,
flat=pol_flat,
valid=pol_valid,
)
policy_opt_inputs = graph_inputs(
"PolicyOptInputs",
obs_var=obs_var,
action_var=action_var,
reward_var=reward_var,
baseline_var=baseline_var,
valid_var=valid_var,
policy_state_info_vars_list=policy_state_info_vars_list,
policy_old_dist_info_vars_list=policy_old_dist_info_vars_list,
)
return policy_loss_inputs, policy_opt_inputs
def _build_graph(self):
self.policy = self.policy_cls(**self.policy_args,
name='ddopg_model_policy')
self.var_list = self.policy.get_params(trainable=True)
self.var_shapes = [var.shape for var in self.var_list]
self.n_params = sum(
[shape.num_elements() for shape in self.var_shapes])
self.policy_dist = self.policy.distribution
self.policy_params_shapes = [
param.shape for param in self.policy.get_params(trainable=True)
]
observation_space = self.policy.observation_space
action_space = self.policy.action_space
self.obs_var = observation_space.new_tensor_variable(
name='obs', extra_dims=2) # (n_paths, H, Dy)
self.action_var = action_space.new_tensor_variable(
name='action', extra_dims=2) # (n_paths, H, Du)
self.path_return_var = tf.placeholder(tf.float32, [None],
'path_return') # (n_paths, )
self.valid_var = tf.placeholder(tf.float32, [None, None],
'valid') # (n_paths, H)
self.train_logps = tf.placeholder(
dtype=tf.float32, shape=[None, None],
name='train_logps_pre') # (n_paths, n_train)
self.log_std_var = tf.placeholder(dtype=tf.float32,
shape=[],
name='log_std')
self.delta_var = tf.placeholder(dtype=tf.float32,
shape=[],
name='delta')
self.input_vars = [
self.obs_var, self.action_var, self.path_return_var,
self.valid_var, self.train_logps, self.delta_var, self.log_std_var
]
# Flatten observation and actions for vectorized computations
self.obs_flat = flatten_batch(self.obs_var,
name='obs_flat') # (n_paths * H, Dy)
self.action_flat = flatten_batch(
self.action_var, name='action_flat') # (n_paths * H, Du)
# Shape of training data: (# of paths, path horizon) = (N_train, H)
self.batch_shape = tf.shape(self.obs_var)[0:2]
# Compute logp for all policy
dist_info_flat = self.policy.dist_info_sym(self.obs_flat,
name='dist_info_flat')
dist_info_flat['log_std'] = self.log_std_var * tf.ones_like(
dist_info_flat['mean'])
test_logp_flat = self.policy_dist.log_likelihood_sym(self.action_flat,
dist_info_flat,
name='logp_flat')
test_logp_full = tf.reshape(test_logp_flat,
self.batch_shape) # (n_epochs, H)
self.test_logps = tf.reduce_sum(test_logp_full * self.valid_var,
axis=1)[None, :]
self.all_logps = tf.concat(
(
self.train_logps, # (n_train + n_test, n_paths)
self.test_logps),
axis=0)
# Prevent exp() overflow by shifting logps
self.logp_max = tf.reduce_max(self.all_logps, axis=0) # (n_paths, )
self.train_logps_0 = self.train_logps - self.logp_max # (n_train, n_paths)
self.test_logps_0 = self.test_logps - self.logp_max # (n_paths, )
self.train_liks = tf.exp(self.train_logps_0) # (n_train, n_paths)
self.test_liks = tf.exp(self.test_logps_0) # (n_paths, )
# Mean traj lik for empirical mixture distribution
self.train_mean_liks = tf.reduce_mean(self.train_liks,
axis=0) + self.eps # (n_paths, )
# Compute prediction for all training policies
train_res = self._compute_prediction_vec(self.train_liks)
self.J_train = train_res[0]
self.J2_train = train_res[1]
self.J_var_train = train_res[2]
self.J_unc_train = train_res[3]
self.w_train = train_res[4]
self.wn_train = train_res[5]
self.ess_train = train_res[6]
# Compute prediction for all test policies
test_res = self._compute_prediction_vec(self.test_liks)
self.J_test = test_res[0]
self.J2_test = test_res[1]
self.J_var_test = test_res[2]
self.J_unc_test = test_res[3]
self.w_test = test_res[4]
self.wn_test = test_res[5]
self.ess_test = test_res[6]
def _build_inputs(self):
"""Decalre graph inputs variables."""
observation_space = self.policy.observation_space
action_space = self.policy.action_space
policy_dist = self.policy.distribution
with tf.name_scope("inputs"):
obs_var = observation_space.new_tensor_variable(
name="obs",
extra_dims=2) # yapf: disable
action_var = action_space.new_tensor_variable(
name="action",
extra_dims=2) # yapf: disable
reward_var = tensor_utils.new_tensor(
name="reward",
ndim=2,
dtype=tf.float32) # yapf: disable
valid_var = tensor_utils.new_tensor(
name="valid",
ndim=2,
dtype=tf.float32) # yapf: disable
feat_diff = tensor_utils.new_tensor(
name="feat_diff",
ndim=2,
dtype=tf.float32) # yapf: disable
param_v = tensor_utils.new_tensor(
name="param_v",
ndim=1,
dtype=tf.float32) # yapf: disable
param_eta = tensor_utils.new_tensor(
name="param_eta",
ndim=0,
dtype=tf.float32) # yapf: disable
policy_state_info_vars = {
k: tf.placeholder(
tf.float32,
shape=[None] * 2 + list(shape),
name=k)
for k, shape in self.policy.state_info_specs
} # yapf: disable
policy_state_info_vars_list = [
policy_state_info_vars[k]
for k in self.policy.state_info_keys
] # yapf: disable
policy_old_dist_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * 2 + list(shape),
name="policy_old_%s" % k)
for k, shape in policy_dist.dist_info_specs
}
policy_old_dist_info_vars_list = [
policy_old_dist_info_vars[k]
for k in policy_dist.dist_info_keys
]
with tf.name_scope("flat"):
obs_flat = flatten_batch(obs_var, name="obs_flat")
action_flat = flatten_batch(action_var, name="action_flat")
reward_flat = flatten_batch(reward_var, name="reward_flat")
valid_flat = flatten_batch(valid_var, name="valid_flat")
feat_diff_flat = flatten_batch(
feat_diff,
name="feat_diff_flat") # yapf: disable
policy_state_info_vars_flat = flatten_batch_dict(
policy_state_info_vars,
name="policy_state_info_vars_flat") # yapf: disable
policy_old_dist_info_vars_flat = flatten_batch_dict(
policy_old_dist_info_vars,
name="policy_old_dist_info_vars_flat")
with tf.name_scope("valid"):
reward_valid = filter_valids(
reward_flat,
valid_flat,
name="reward_valid") # yapf: disable
action_valid = filter_valids(
action_flat,
valid_flat,
name="action_valid") # yapf: disable
policy_state_info_vars_valid = filter_valids_dict(
policy_state_info_vars_flat,
valid_flat,
name="policy_state_info_vars_valid")
policy_old_dist_info_vars_valid = filter_valids_dict(
policy_old_dist_info_vars_flat,
valid_flat,
name="policy_old_dist_info_vars_valid")
pol_flat = graph_inputs(
"PolicyLossInputsFlat",
obs_var=obs_flat,
action_var=action_flat,
reward_var=reward_flat,
valid_var=valid_flat,
feat_diff=feat_diff_flat,
policy_state_info_vars=policy_state_info_vars_flat,
policy_old_dist_info_vars=policy_old_dist_info_vars_flat,
)
pol_valid = graph_inputs(
"PolicyLossInputsValid",
reward_var=reward_valid,
action_var=action_valid,
policy_state_info_vars=policy_state_info_vars_valid,
policy_old_dist_info_vars=policy_old_dist_info_vars_valid,
)
policy_loss_inputs = graph_inputs(
"PolicyLossInputs",
obs_var=obs_var,
action_var=action_var,
reward_var=reward_var,
valid_var=valid_var,
feat_diff=feat_diff,
param_eta=param_eta,
param_v=param_v,
policy_state_info_vars=policy_state_info_vars,
policy_old_dist_info_vars=policy_old_dist_info_vars,
flat=pol_flat,
valid=pol_valid,
)
policy_opt_inputs = graph_inputs(
"PolicyOptInputs",
obs_var=obs_var,
action_var=action_var,
reward_var=reward_var,
valid_var=valid_var,
feat_diff=feat_diff,
param_eta=param_eta,
param_v=param_v,
policy_state_info_vars_list=policy_state_info_vars_list,
policy_old_dist_info_vars_list=policy_old_dist_info_vars_list,
)
dual_opt_inputs = graph_inputs(
"DualOptInputs",
reward_var=reward_var,
valid_var=valid_var,
feat_diff=feat_diff,
param_eta=param_eta,
param_v=param_v,
policy_state_info_vars_list=policy_state_info_vars_list,
policy_old_dist_info_vars_list=policy_old_dist_info_vars_list,
)
return policy_loss_inputs, policy_opt_inputs, dual_opt_inputs
def _build_policy_loss(self, i):
pol_dist = self.policy.distribution
policy_entropy = self._build_entropy_term(i)
rewards = i.reward_var
if self._maximum_entropy:
with tf.name_scope('augmented_rewards'):
rewards = i.reward_var + self.policy_ent_coeff * policy_entropy
with tf.name_scope('policy_loss'):
adv = compute_advantages(self.discount,
self.gae_lambda,
self.max_path_length,
i.baseline_var,
rewards,
name='adv')
adv_flat = flatten_batch(adv, name='adv_flat')
adv_valid = filter_valids(adv_flat,
i.flat.valid_var,
name='adv_valid')
if self.policy.recurrent:
adv = tf.reshape(adv, [-1, self.max_path_length])
# Optionally normalize advantages
eps = tf.constant(1e-8, dtype=tf.float32)
if self.center_adv:
if self.policy.recurrent:
adv = center_advs(adv, axes=[0], eps=eps)
else:
adv_valid = center_advs(adv_valid, axes=[0], eps=eps)
if self.positive_adv:
if self.policy.recurrent:
adv = positive_advs(adv, eps)
else:
adv_valid = positive_advs(adv_valid, eps)
if self.policy.recurrent:
policy_dist_info = self.policy.dist_info_sym(
i.obs_var,
i.policy_state_info_vars,
name='policy_dist_info')
else:
policy_dist_info_flat = self.policy.dist_info_sym(
i.flat.obs_var,
i.flat.policy_state_info_vars,
name='policy_dist_info_flat')
policy_dist_info_valid = filter_valids_dict(
policy_dist_info_flat,
i.flat.valid_var,
name='policy_dist_info_valid')
policy_dist_info = policy_dist_info_valid
# Calculate loss function and KL divergence
with tf.name_scope('kl'):
if self.policy.recurrent:
kl = pol_dist.kl_sym(
i.policy_old_dist_info_vars,
policy_dist_info,
)
pol_mean_kl = tf.reduce_sum(
kl * i.valid_var) / tf.reduce_sum(i.valid_var)
else:
kl = pol_dist.kl_sym(
i.valid.policy_old_dist_info_vars,
policy_dist_info_valid,
)
pol_mean_kl = tf.reduce_mean(kl)
# Calculate vanilla loss
with tf.name_scope('vanilla_loss'):
if self.policy.recurrent:
ll = pol_dist.log_likelihood_sym(i.action_var,
policy_dist_info,
name='log_likelihood')
vanilla = ll * adv * i.valid_var
else:
ll = pol_dist.log_likelihood_sym(i.valid.action_var,
policy_dist_info_valid,
name='log_likelihood')
vanilla = ll * adv_valid
# Calculate surrogate loss
with tf.name_scope('surrogate_loss'):
if self.policy.recurrent:
lr = pol_dist.likelihood_ratio_sym(
i.action_var,
i.policy_old_dist_info_vars,
policy_dist_info,
name='lr')
surrogate = lr * adv * i.valid_var
else:
lr = pol_dist.likelihood_ratio_sym(
i.valid.action_var,
i.valid.policy_old_dist_info_vars,
policy_dist_info_valid,
name='lr')
surrogate = lr * adv_valid
# Finalize objective function
with tf.name_scope('loss'):
if self._pg_loss == 'vanilla':
# VPG uses the vanilla objective
obj = tf.identity(vanilla, name='vanilla_obj')
elif self._pg_loss == 'surrogate':
# TRPO uses the standard surrogate objective
obj = tf.identity(surrogate, name='surr_obj')
elif self._pg_loss == 'surrogate_clip':
lr_clip = tf.clip_by_value(lr,
1 - self.lr_clip_range,
1 + self.lr_clip_range,
name='lr_clip')
if self.policy.recurrent:
surr_clip = lr_clip * adv * i.valid_var
else:
surr_clip = lr_clip * adv_valid
obj = tf.minimum(surrogate, surr_clip, name='surr_obj')
if self._entropy_regularzied:
obj += self.policy_ent_coeff * policy_entropy
# Maximize E[surrogate objective] by minimizing
# -E_t[surrogate objective]
if self.policy.recurrent:
loss = -tf.reduce_sum(obj) / tf.reduce_sum(i.valid_var)
else:
loss = -tf.reduce_mean(obj)
# Diagnostic functions
self.f_policy_kl = tensor_utils.compile_function(
flatten_inputs(self._policy_opt_inputs),
pol_mean_kl,
log_name='f_policy_kl')
self.f_rewards = tensor_utils.compile_function(
flatten_inputs(self._policy_opt_inputs),
rewards,
log_name='f_rewards')
returns = discounted_returns(self.discount, self.max_path_length,
rewards)
self.f_returns = tensor_utils.compile_function(
flatten_inputs(self._policy_opt_inputs),
returns,
log_name='f_returns')
return loss, pol_mean_kl
def _build_inputs(self):
observation_space = self.policy.observation_space
action_space = self.policy.action_space
policy_dist = self.policy.distribution
with tf.name_scope('inputs'):
if self.flatten_input:
obs_var = tf.compat.v1.placeholder(
tf.float32,
shape=[None, None, observation_space.flat_dim],
name='obs')
else:
obs_var = observation_space.to_tf_placeholder(name='obs',
batch_dims=2)
action_var = action_space.to_tf_placeholder(name='action',
batch_dims=2)
reward_var = tensor_utils.new_tensor(name='reward',
ndim=2,
dtype=tf.float32)
valid_var = tf.compat.v1.placeholder(tf.float32,
shape=[None, None],
name='valid')
baseline_var = tensor_utils.new_tensor(name='baseline',
ndim=2,
dtype=tf.float32)
policy_state_info_vars = {
k: tf.compat.v1.placeholder(tf.float32,
shape=[None] * 2 + list(shape),
name=k)
for k, shape in self.policy.state_info_specs
}
policy_state_info_vars_list = [
policy_state_info_vars[k] for k in self.policy.state_info_keys
]
# old policy distribution
policy_old_dist_info_vars = {
k: tf.compat.v1.placeholder(tf.float32,
shape=[None] * 2 + list(shape),
name='policy_old_%s' % k)
for k, shape in policy_dist.dist_info_specs
}
policy_old_dist_info_vars_list = [
policy_old_dist_info_vars[k]
for k in policy_dist.dist_info_keys
]
# flattened view
with tf.name_scope('flat'):
obs_flat = flatten_batch(obs_var, name='obs_flat')
action_flat = flatten_batch(action_var, name='action_flat')
reward_flat = flatten_batch(reward_var, name='reward_flat')
valid_flat = flatten_batch(valid_var, name='valid_flat')
policy_state_info_vars_flat = flatten_batch_dict(
policy_state_info_vars, name='policy_state_info_vars_flat')
policy_old_dist_info_vars_flat = flatten_batch_dict(
policy_old_dist_info_vars,
name='policy_old_dist_info_vars_flat')
# valid view
with tf.name_scope('valid'):
action_valid = filter_valids(action_flat,
valid_flat,
name='action_valid')
policy_state_info_vars_valid = filter_valids_dict(
policy_state_info_vars_flat,
valid_flat,
name='policy_state_info_vars_valid')
policy_old_dist_info_vars_valid = filter_valids_dict(
policy_old_dist_info_vars_flat,
valid_flat,
name='policy_old_dist_info_vars_valid')
# policy loss and optimizer inputs
pol_flat = graph_inputs(
'PolicyLossInputsFlat',
obs_var=obs_flat,
action_var=action_flat,
reward_var=reward_flat,
valid_var=valid_flat,
policy_state_info_vars=policy_state_info_vars_flat,
policy_old_dist_info_vars=policy_old_dist_info_vars_flat,
)
pol_valid = graph_inputs(
'PolicyLossInputsValid',
action_var=action_valid,
policy_state_info_vars=policy_state_info_vars_valid,
policy_old_dist_info_vars=policy_old_dist_info_vars_valid,
)
policy_loss_inputs = graph_inputs(
'PolicyLossInputs',
obs_var=obs_var,
action_var=action_var,
reward_var=reward_var,
baseline_var=baseline_var,
valid_var=valid_var,
policy_state_info_vars=policy_state_info_vars,
policy_old_dist_info_vars=policy_old_dist_info_vars,
flat=pol_flat,
valid=pol_valid,
)
policy_opt_inputs = graph_inputs(
'PolicyOptInputs',
obs_var=obs_var,
action_var=action_var,
reward_var=reward_var,
baseline_var=baseline_var,
valid_var=valid_var,
policy_state_info_vars_list=policy_state_info_vars_list,
policy_old_dist_info_vars_list=policy_old_dist_info_vars_list,
)
return policy_loss_inputs, policy_opt_inputs
def _build_policy_loss(self, i):
pol_dist = self.policy.distribution
policy_entropy = self._build_entropy_term(i)
with tf.name_scope('augmented_rewards'):
rewards = i.reward_var + (self.policy_ent_coeff * policy_entropy)
with tf.name_scope('policy_loss'):
advantages = compute_advantages(self.discount,
self.gae_lambda,
self.max_path_length,
i.baseline_var,
rewards,
name='advantages')
adv_flat = flatten_batch(advantages, name='adv_flat')
adv_valid = filter_valids(adv_flat,
i.flat.valid_var,
name='adv_valid')
if self.policy.recurrent:
advantages = tf.reshape(advantages, [-1, self.max_path_length])
# Optionally normalize advantages
eps = tf.constant(1e-8, dtype=tf.float32)
if self.center_adv:
with tf.name_scope('center_adv'):
mean, var = tf.nn.moments(adv_valid, axes=[0])
adv_valid = tf.nn.batch_normalization(
adv_valid, mean, var, 0, 1, eps)
if self.positive_adv:
with tf.name_scope('positive_adv'):
m = tf.reduce_min(adv_valid)
adv_valid = (adv_valid - m) + eps
if self.policy.recurrent:
policy_dist_info = self.policy.dist_info_sym(
i.obs_var,
i.policy_state_info_vars,
name='policy_dist_info')
else:
policy_dist_info_flat = self.policy.dist_info_sym(
i.flat.obs_var,
i.flat.policy_state_info_vars,
name='policy_dist_info_flat')
policy_dist_info_valid = filter_valids_dict(
policy_dist_info_flat,
i.flat.valid_var,
name='policy_dist_info_valid')
# Calculate loss function and KL divergence
with tf.name_scope('kl'):
if self.policy.recurrent:
kl = pol_dist.kl_sym(
i.policy_old_dist_info_vars,
policy_dist_info,
)
pol_mean_kl = tf.reduce_sum(
kl * i.valid_var) / tf.reduce_sum(i.valid_var)
else:
kl = pol_dist.kl_sym(
i.valid.policy_old_dist_info_vars,
policy_dist_info_valid,
)
pol_mean_kl = tf.reduce_mean(kl)
# Calculate surrogate loss
with tf.name_scope('surr_loss'):
if self.policy.recurrent:
lr = pol_dist.likelihood_ratio_sym(
i.action_var,
i.policy_old_dist_info_vars,
policy_dist_info,
name='lr')
surr_vanilla = lr * advantages * i.valid_var
else:
lr = pol_dist.likelihood_ratio_sym(
i.valid.action_var,
i.valid.policy_old_dist_info_vars,
policy_dist_info_valid,
name='lr')
surr_vanilla = lr * adv_valid
if self._pg_loss == PGLoss.VANILLA:
# VPG, TRPO use the standard surrogate objective
surr_obj = tf.identity(surr_vanilla, name='surr_obj')
elif self._pg_loss == PGLoss.CLIP:
lr_clip = tf.clip_by_value(lr,
1 - self.clip_range,
1 + self.clip_range,
name='lr_clip')
if self.policy.recurrent:
surr_clip = lr_clip * advantages * i.valid_var
else:
surr_clip = lr_clip * adv_valid
surr_obj = tf.minimum(surr_vanilla,
surr_clip,
name='surr_obj')
else:
raise NotImplementedError('Unknown PGLoss')
# Maximize E[surrogate objective] by minimizing
# -E_t[surrogate objective]
if self.policy.recurrent:
surr_loss = (-tf.reduce_sum(surr_vanilla)) / tf.reduce_sum(
i.valid_var)
else:
surr_loss = -tf.reduce_mean(surr_obj)
# Diagnostic functions
self.f_policy_kl = compile_function(flatten_inputs(
self._policy_opt_inputs),
pol_mean_kl,
log_name='f_policy_kl')
self.f_rewards = compile_function(flatten_inputs(
self._policy_opt_inputs),
rewards,
log_name='f_rewards')
returns = discounted_returns(self.discount, self.max_path_length,
rewards)
self.f_returns = compile_function(flatten_inputs(
self._policy_opt_inputs),
returns,
log_name='f_returns')
return surr_loss, pol_mean_kl
