def _create_cc_actor(
self,
encoded: tf.Tensor,
tanh_squash: bool = False,
reparameterize: bool = False,
condition_sigma_on_obs: bool = True,
) -> None:
"""
Creates Continuous control actor-critic model.
:param h_size: Size of hidden linear layers.
:param num_layers: Number of hidden linear layers.
:param vis_encode_type: Type of visual encoder to use if visual input.
:param tanh_squash: Whether to use a tanh function, or a clipped output.
:param reparameterize: Whether we are using the resampling trick to update the policy.
"""
if self.use_recurrent:
self.memory_in = tf.placeholder(shape=[None, self.m_size],
dtype=tf.float32,
name="recurrent_in")
hidden_policy, memory_policy_out = ModelUtils.create_recurrent_encoder(
encoded,
self.memory_in,
self.sequence_length_ph,
name="lstm_policy")
self.memory_out = tf.identity(memory_policy_out,
name="recurrent_out")
else:
hidden_policy = encoded
with tf.variable_scope("policy"):
distribution = GaussianDistribution(
hidden_policy,
self.act_size,
reparameterize=reparameterize,
tanh_squash=tanh_squash,
condition_sigma=condition_sigma_on_obs,
)
if tanh_squash:
self.output_pre = distribution.sample
self.output = tf.identity(self.output_pre, name="action")
else:
self.output_pre = distribution.sample
# Clip and scale output to ensure actions are always within [-1, 1] range.
output_post = tf.clip_by_value(self.output_pre, -3, 3) / 3
self.output = tf.identity(output_post, name="action")
self.selected_actions = tf.stop_gradient(self.output)
self.all_log_probs = tf.identity(distribution.log_probs,
name="action_probs")
self.entropy = distribution.entropy
# We keep these tensors the same name, but use new nodes to keep code parallelism with discrete control.
self.total_log_probs = distribution.total_log_probs
def _create_dc_actor(self, encoded: tf.Tensor) -> None:
"""
Creates Discrete control actor-critic model.
:param h_size: Size of hidden linear layers.
:param num_layers: Number of hidden linear layers.
:param vis_encode_type: Type of visual encoder to use if visual input.
"""
if self.use_recurrent:
self.prev_action = tf.placeholder(shape=[None,
len(self.act_size)],
dtype=tf.int32,
name="prev_action")
prev_action_oh = tf.concat(
[
tf.one_hot(self.prev_action[:, i], self.act_size[i])
for i in range(len(self.act_size))
],
axis=1,
)
hidden_policy = tf.concat([encoded, prev_action_oh], axis=1)
self.memory_in = tf.placeholder(shape=[None, self.m_size],
dtype=tf.float32,
name="recurrent_in")
hidden_policy, memory_policy_out = ModelUtils.create_recurrent_encoder(
hidden_policy,
self.memory_in,
self.sequence_length_ph,
name="lstm_policy",
)
self.memory_out = tf.identity(memory_policy_out, "recurrent_out")
else:
hidden_policy = encoded
self.action_masks = tf.placeholder(shape=[None,
sum(self.act_size)],
dtype=tf.float32,
name="action_masks")
with tf.variable_scope("policy"):
distribution = MultiCategoricalDistribution(
hidden_policy, self.act_size, self.action_masks)
# It's important that we are able to feed_dict a value into this tensor to get the
# right one-hot encoding, so we can't do identity on it.
self.output = distribution.sample
self.all_log_probs = tf.identity(distribution.log_probs, name="action")
self.selected_actions = tf.stop_gradient(
distribution.sample_onehot) # In discrete, these are onehot
self.entropy = distribution.entropy
self.total_log_probs = distribution.total_log_probs
def _create_inputs_and_outputs(self) -> None:
"""
Assign the higher-level SACModel's inputs and outputs to those of its policy or
target network.
"""
self.vector_in = self.policy.vector_in
self.visual_in = self.policy.visual_in
self.next_vector_in = self.target_network.vector_in
self.next_visual_in = self.target_network.visual_in
self.sequence_length_ph = self.policy.sequence_length_ph
self.next_sequence_length_ph = self.target_network.sequence_length_ph
if not self.policy.use_continuous_act:
self.action_masks = self.policy_network.action_masks
else:
self.output_pre = self.policy_network.output_pre
# Don't use value estimate during inference.
self.value = tf.identity(self.policy_network.value,
name="value_estimate_unused")
self.value_heads = self.policy_network.value_heads
self.dones_holder = tf.placeholder(shape=[None],
dtype=tf.float32,
name="dones_holder")
if self.policy.use_recurrent:
self.memory_in = self.policy_network.memory_in
self.memory_out = self.policy_network.memory_out
if not self.policy.use_continuous_act:
self.prev_action = self.policy_network.prev_action
self.next_memory_in = self.target_network.memory_in
def create_inputs_and_outputs(self):
"""
Assign the higher-level SACModel's inputs and outputs to those of its policy or
target network.
"""
self.vector_in = self.policy_network.vector_in
self.visual_in = self.policy_network.visual_in
self.next_vector_in = self.target_network.vector_in
self.next_visual_in = self.target_network.visual_in
self.action_holder = self.policy_network.action_holder
self.sequence_length = self.policy_network.sequence_length
self.next_sequence_length = self.target_network.sequence_length
if self.brain.vector_action_space_type == "discrete":
self.action_masks = self.policy_network.action_masks
else:
self.output_pre = self.policy_network.output_pre
self.output = self.policy_network.output
# Don't use value estimate during inference. TODO: Check why PPO uses value_estimate in inference.
self.value = tf.identity(
self.policy_network.value, name="value_estimate_unused"
)
self.value_heads = self.policy_network.value_heads
self.all_log_probs = self.policy_network.all_log_probs
self.dones_holder = tf.placeholder(
shape=[None], dtype=tf.float32, name="dones_holder"
)
# This is just a dummy to get pretraining to work. PPO has this but SAC doesn't.
# TODO: Proper input and output specs for models
self.epsilon = tf.placeholder(
shape=[None, self.act_size[0]], dtype=tf.float32, name="epsilon"
)
if self.use_recurrent:
self.memory_in = self.policy_network.memory_in
self.memory_out = self.policy_network.memory_out
# For Barracuda
self.inference_memory_out = tf.identity(
self.policy_network.policy_memory_out, name="recurrent_out"
)
if self.brain.vector_action_space_type == "discrete":
self.prev_action = self.policy_network.prev_action
self.next_memory_in = self.target_network.memory_in
def _create_cc_critic(
self, h_size: int, num_layers: int, vis_encode_type: EncoderType
) -> None:
"""
Creates Continuous control critic (value) network.
:param h_size: Size of hidden linear layers.
:param num_layers: Number of hidden linear layers.
:param vis_encode_type: The type of visual encoder to use.
"""
hidden_stream = ModelUtils.create_observation_streams(
self.policy.visual_in,
self.policy.processed_vector_in,
1,
h_size,
num_layers,
vis_encode_type,
)[0]
if self.policy.use_recurrent:
hidden_value, memory_value_out = ModelUtils.create_recurrent_encoder(
hidden_stream,
self.memory_in,
self.policy.sequence_length_ph,
name="lstm_value",
)
self.memory_out = memory_value_out
else:
hidden_value = hidden_stream
self.value_heads, self.value = ModelUtils.create_value_heads(
self.stream_names, hidden_value
)
self.all_old_log_probs = tf.placeholder(
shape=[None, sum(self.policy.act_size)],
dtype=tf.float32,
name="old_probabilities",
)
self.old_log_probs = tf.reduce_sum(
(tf.identity(self.all_old_log_probs)), axis=1, keepdims=True
)
def make_inputs(self) -> None:
"""
Creates the input layers for the discriminator
"""
self.done_expert = tf.placeholder(shape=[None, 1], dtype=tf.float32)
self.done_policy = tf.placeholder(shape=[None, 1], dtype=tf.float32)
if self.policy.behavior_spec.action_spec.is_continuous():
action_length = self.policy.act_size[0]
self.action_in_expert = tf.placeholder(shape=[None, action_length],
dtype=tf.float32)
self.expert_action = tf.identity(self.action_in_expert)
else:
action_length = len(self.policy.act_size)
self.action_in_expert = tf.placeholder(shape=[None, action_length],
dtype=tf.int32)
self.expert_action = tf.concat(
[
tf.one_hot(self.action_in_expert[:, i], act_size)
for i, act_size in enumerate(self.policy.act_size)
],
axis=1,
)
def make_inputs(self) -> None:
"""
Creates the input layers for the discriminator
"""
self.done_expert_holder = tf.placeholder(shape=[None], dtype=tf.float32)
self.done_policy_holder = tf.placeholder(shape=[None], dtype=tf.float32)
self.done_expert = tf.expand_dims(self.done_expert_holder, -1)
self.done_policy = tf.expand_dims(self.done_policy_holder, -1)
if self.policy.brain.vector_action_space_type == "continuous":
action_length = self.policy.act_size[0]
self.action_in_expert = tf.placeholder(
shape=[None, action_length], dtype=tf.float32
)
self.expert_action = tf.identity(self.action_in_expert)
else:
action_length = len(self.policy.act_size)
self.action_in_expert = tf.placeholder(
shape=[None, action_length], dtype=tf.int32
)
self.expert_action = tf.concat(
[
tf.one_hot(self.action_in_expert[:, i], act_size)
for i, act_size in enumerate(self.policy.act_size)
],
axis=1,
)
encoded_policy_list = []
encoded_expert_list = []
if self.policy.vec_obs_size > 0:
self.obs_in_expert = tf.placeholder(
shape=[None, self.policy.vec_obs_size], dtype=tf.float32
)
if self.policy.normalize:
encoded_expert_list.append(
ModelUtils.normalize_vector_obs(
self.obs_in_expert,
self.policy.running_mean,
self.policy.running_variance,
self.policy.normalization_steps,
)
)
encoded_policy_list.append(self.policy.processed_vector_in)
else:
encoded_expert_list.append(self.obs_in_expert)
encoded_policy_list.append(self.policy.vector_in)
if self.policy.vis_obs_size > 0:
self.expert_visual_in: List[tf.Tensor] = []
visual_policy_encoders = []
visual_expert_encoders = []
for i in range(self.policy.vis_obs_size):
# Create input ops for next (t+1) visual observations.
visual_input = ModelUtils.create_visual_input(
self.policy.brain.camera_resolutions[i],
name="gail_visual_observation_" + str(i),
)
self.expert_visual_in.append(visual_input)
encoded_policy_visual = ModelUtils.create_visual_observation_encoder(
self.policy.visual_in[i],
self.encoding_size,
ModelUtils.swish,
1,
"gail_stream_{}_visual_obs_encoder".format(i),
False,
)
encoded_expert_visual = ModelUtils.create_visual_observation_encoder(
self.expert_visual_in[i],
self.encoding_size,
ModelUtils.swish,
1,
"gail_stream_{}_visual_obs_encoder".format(i),
True,
)
visual_policy_encoders.append(encoded_policy_visual)
visual_expert_encoders.append(encoded_expert_visual)
hidden_policy_visual = tf.concat(visual_policy_encoders, axis=1)
hidden_expert_visual = tf.concat(visual_expert_encoders, axis=1)
encoded_policy_list.append(hidden_policy_visual)
encoded_expert_list.append(hidden_expert_visual)
self.encoded_expert = tf.concat(encoded_expert_list, axis=1)
self.encoded_policy = tf.concat(encoded_policy_list, axis=1)
def create_dc_actor(self, hidden_policy, scope):
"""
Creates Discrete control actor for SAC.
:param hidden_policy: Output of feature extractor (i.e. the input for vector obs, output of CNN for visual obs).
:param num_layers: TF scope to assign whatever is created in this block.
"""
scope = self.join_scopes(scope, "policy")
# Create inputs outside of the scope
self.action_masks = tf.placeholder(shape=[None,
sum(self.act_size)],
dtype=tf.float32,
name="action_masks")
if self.use_recurrent:
self.prev_action = tf.placeholder(shape=[None,
len(self.act_size)],
dtype=tf.int32,
name="prev_action")
with tf.variable_scope(scope):
hidden_policy = self.create_vector_observation_encoder(
hidden_policy,
self.h_size,
self.activ_fn,
self.num_layers,
"encoder",
False,
)
if self.use_recurrent:
prev_action_oh = tf.concat(
[
tf.one_hot(self.prev_action[:, i], self.act_size[i])
for i in range(len(self.act_size))
],
axis=1,
)
hidden_policy = tf.concat([hidden_policy, prev_action_oh], axis=1)
hidden_policy, memory_out = self.create_recurrent_encoder(
hidden_policy,
self.policy_memory_in,
self.sequence_length,
name="lstm_policy",
)
self.policy_memory_out = memory_out
with tf.variable_scope(scope):
policy_branches = []
for size in self.act_size:
policy_branches.append(
tf.layers.dense(
hidden_policy,
size,
activation=None,
use_bias=False,
kernel_initializer=tf.initializers.variance_scaling(
0.01),
))
all_logits = tf.concat(policy_branches,
axis=1,
name="action_probs")
output, normalized_probs, normalized_logprobs = self.create_discrete_action_masking_layer(
all_logits, self.action_masks, self.act_size)
self.action_probs = normalized_probs
# Really, this is entropy, but it has an analogous purpose to the log probs in the
# continuous case.
self.all_log_probs = self.action_probs * normalized_logprobs
self.output = output
# Create action input (discrete)
self.action_holder = tf.placeholder(
shape=[None, len(policy_branches)],
dtype=tf.int32,
name="action_holder")
self.output_oh = tf.concat(
[
tf.one_hot(self.action_holder[:, i], self.act_size[i])
for i in range(len(self.act_size))
],
axis=1,
)
# For Curiosity and GAIL to retrieve selected actions. We don't
# need the mask at this point because it's already stored in the buffer.
self.selected_actions = tf.stop_gradient(self.output_oh)
self.external_action_in = tf.concat(
[
tf.one_hot(self.action_holder[:, i], self.act_size[i])
for i in range(len(self.act_size))
],
axis=1,
)
# This is total entropy over all branches
self.entropy = -1 * tf.reduce_sum(self.all_log_probs, axis=1)
# Extract the normalized logprobs for Barracuda
self.normalized_logprobs = tf.identity(normalized_logprobs,
name="action")
# We kept the LSTMs at a different scope than the rest, so add them if they exist.
self.policy_vars = self.get_vars(scope)
if self.use_recurrent:
self.policy_vars += self.get_vars("lstm")
def create_cc_actor(self, hidden_policy, scope):
"""
Creates Continuous control actor for SAC.
:param hidden_policy: Output of feature extractor (i.e. the input for vector obs, output of CNN for visual obs).
:param num_layers: TF scope to assign whatever is created in this block.
"""
# Create action input (continuous)
self.action_holder = tf.placeholder(shape=[None, self.act_size[0]],
dtype=tf.float32,
name="action_holder")
self.external_action_in = self.action_holder
scope = self.join_scopes(scope, "policy")
with tf.variable_scope(scope):
hidden_policy = self.create_vector_observation_encoder(
hidden_policy,
self.h_size,
self.activ_fn,
self.num_layers,
"encoder",
False,
)
if self.use_recurrent:
hidden_policy, memory_out = self.create_recurrent_encoder(
hidden_policy,
self.policy_memory_in,
self.sequence_length,
name="lstm_policy",
)
self.policy_memory_out = memory_out
with tf.variable_scope(scope):
mu = tf.layers.dense(
hidden_policy,
self.act_size[0],
activation=None,
name="mu",
kernel_initializer=LearningModel.scaled_init(0.01),
)
# Policy-dependent log_sigma_sq
log_sigma_sq = tf.layers.dense(
hidden_policy,
self.act_size[0],
activation=None,
name="log_std",
kernel_initializer=LearningModel.scaled_init(0.01),
)
self.log_sigma_sq = tf.clip_by_value(log_sigma_sq, LOG_STD_MIN,
LOG_STD_MAX)
sigma_sq = tf.exp(self.log_sigma_sq)
# Do the reparameterization trick
policy_ = mu + tf.random_normal(tf.shape(mu)) * sigma_sq
_gauss_pre = -0.5 * (((policy_ - mu) /
(tf.exp(self.log_sigma_sq) + EPSILON))**2 +
2 * self.log_sigma_sq + np.log(2 * np.pi))
all_probs = tf.reduce_sum(_gauss_pre, axis=1, keepdims=True)
self.entropy = tf.reduce_sum(self.log_sigma_sq +
0.5 * np.log(2.0 * np.pi * np.e),
axis=-1)
# Squash probabilities
# Keep deterministic around in case we want to use it.
self.deterministic_output = tf.tanh(mu)
# Note that this is just for symmetry with PPO.
self.output_pre = tf.tanh(policy_)
# Squash correction
all_probs -= tf.reduce_sum(tf.log(1 - self.output_pre**2 +
EPSILON),
axis=1,
keepdims=True)
self.all_log_probs = all_probs
self.selected_actions = tf.stop_gradient(self.output_pre)
self.action_probs = all_probs
# Extract output for Barracuda
self.output = tf.identity(self.output_pre, name="action")
# Get all policy vars
self.policy_vars = self.get_vars(scope)
def make_inputs(self) -> None:
"""
Creates the input layers for the discriminator
"""
self.done_expert_holder = tf.placeholder(shape=[None], dtype=tf.float32)
self.done_policy_holder = tf.placeholder(shape=[None], dtype=tf.float32)
self.done_expert = tf.expand_dims(self.done_expert_holder, -1)
self.done_policy = tf.expand_dims(self.done_policy_holder, -1)
if self.policy.behavior_spec.is_action_continuous():
action_length = self.policy.act_size[0]
self.action_in_expert = tf.placeholder(
shape=[None, action_length], dtype=tf.float32
)
self.expert_action = tf.identity(self.action_in_expert)
else:
action_length = len(self.policy.act_size)
self.action_in_expert = tf.placeholder(
shape=[None, action_length], dtype=tf.int32
)
self.expert_action = tf.concat(
[
tf.one_hot(self.action_in_expert[:, i], act_size)
for i, act_size in enumerate(self.policy.act_size)
],
axis=1,
)
encoded_policy_list = []
encoded_expert_list = []
(
self.obs_in_expert,
self.expert_visual_in,
) = ModelUtils.create_input_placeholders(
self.policy.behavior_spec.observation_shapes, "gail_"
)
if self.policy.vec_obs_size > 0:
if self.policy.normalize:
encoded_expert_list.append(
ModelUtils.normalize_vector_obs(
self.obs_in_expert,
self.policy.running_mean,
self.policy.running_variance,
self.policy.normalization_steps,
)
)
encoded_policy_list.append(self.policy.processed_vector_in)
else:
encoded_expert_list.append(self.obs_in_expert)
encoded_policy_list.append(self.policy.vector_in)
if self.expert_visual_in:
visual_policy_encoders = []
visual_expert_encoders = []
for i, (vis_in, exp_vis_in) in enumerate(
zip(self.policy.visual_in, self.expert_visual_in)
):
encoded_policy_visual = ModelUtils.create_visual_observation_encoder(
vis_in,
self.encoding_size,
ModelUtils.swish,
1,
f"gail_stream_{i}_visual_obs_encoder",
False,
)
encoded_expert_visual = ModelUtils.create_visual_observation_encoder(
exp_vis_in,
self.encoding_size,
ModelUtils.swish,
1,
f"gail_stream_{i}_visual_obs_encoder",
True,
)
visual_policy_encoders.append(encoded_policy_visual)
visual_expert_encoders.append(encoded_expert_visual)
hidden_policy_visual = tf.concat(visual_policy_encoders, axis=1)
hidden_expert_visual = tf.concat(visual_expert_encoders, axis=1)
encoded_policy_list.append(hidden_policy_visual)
encoded_expert_list.append(hidden_expert_visual)
self.encoded_expert = tf.concat(encoded_expert_list, axis=1)
self.encoded_policy = tf.concat(encoded_policy_list, axis=1)
def __init__(
self,
brain,
h_size=128,
lr=1e-4,
n_layers=2,
m_size=128,
normalize=False,
use_recurrent=False,
seed=0,
):
LearningModel.__init__(self, m_size, normalize, use_recurrent, brain, seed)
num_streams = 1
hidden_streams = self.create_observation_streams(num_streams, h_size, n_layers)
hidden = hidden_streams[0]
self.dropout_rate = tf.placeholder(
dtype=tf.float32, shape=[], name="dropout_rate"
)
hidden_reg = tf.layers.dropout(hidden, self.dropout_rate)
if self.use_recurrent:
tf.Variable(
self.m_size, name="memory_size", trainable=False, dtype=tf.int32
)
self.memory_in = tf.placeholder(
shape=[None, self.m_size], dtype=tf.float32, name="recurrent_in"
)
hidden_reg, self.memory_out = self.create_recurrent_encoder(
hidden_reg, self.memory_in, self.sequence_length
)
self.memory_out = tf.identity(self.memory_out, name="recurrent_out")
if brain.vector_action_space_type == "discrete":
policy_branches = []
for size in self.act_size:
policy_branches.append(
tf.layers.dense(
hidden_reg,
size,
activation=None,
use_bias=False,
kernel_initializer=tf.initializers.variance_scaling(0.01),
)
)
self.action_probs = tf.concat(
[tf.nn.softmax(branch) for branch in policy_branches],
axis=1,
name="action_probs",
)
self.action_masks = tf.placeholder(
shape=[None, sum(self.act_size)], dtype=tf.float32, name="action_masks"
)
self.sample_action_float, _, normalized_logits = self.create_discrete_action_masking_layer(
tf.concat(policy_branches, axis=1), self.action_masks, self.act_size
)
tf.identity(normalized_logits, name="action")
self.sample_action = tf.cast(self.sample_action_float, tf.int32)
self.true_action = tf.placeholder(
shape=[None, len(policy_branches)],
dtype=tf.int32,
name="teacher_action",
)
self.action_oh = tf.concat(
[
tf.one_hot(self.true_action[:, i], self.act_size[i])
for i in range(len(self.act_size))
],
axis=1,
)
self.loss = tf.reduce_sum(
-tf.log(self.action_probs + 1e-10) * self.action_oh
)
self.action_percent = tf.reduce_mean(
tf.cast(
tf.equal(
tf.cast(tf.argmax(self.action_probs, axis=1), tf.int32),
self.sample_action,
),
tf.float32,
)
)
else:
self.policy = tf.layers.dense(
hidden_reg,
self.act_size[0],
activation=None,
use_bias=False,
name="pre_action",
kernel_initializer=tf.initializers.variance_scaling(0.01),
)
self.clipped_sample_action = tf.clip_by_value(self.policy, -1, 1)
self.sample_action = tf.identity(self.clipped_sample_action, name="action")
self.true_action = tf.placeholder(
shape=[None, self.act_size[0]], dtype=tf.float32, name="teacher_action"
)
self.clipped_true_action = tf.clip_by_value(self.true_action, -1, 1)
self.loss = tf.reduce_sum(
tf.squared_difference(self.clipped_true_action, self.sample_action)
)
optimizer = tf.train.AdamOptimizer(learning_rate=lr)
self.update = optimizer.minimize(self.loss)
def create_cc_actor_critic(self, h_size: int, num_layers: int,
vis_encode_type: EncoderType) -> None:
"""
Creates Continuous control actor-critic model.
:param h_size: Size of hidden linear layers.
:param num_layers: Number of hidden linear layers.
"""
hidden_streams = self.create_observation_streams(
2, h_size, num_layers, vis_encode_type)
if self.use_recurrent:
self.memory_in = tf.placeholder(shape=[None, self.m_size],
dtype=tf.float32,
name="recurrent_in")
_half_point = int(self.m_size / 2)
hidden_policy, memory_policy_out = self.create_recurrent_encoder(
hidden_streams[0],
self.memory_in[:, :_half_point],
self.sequence_length,
name="lstm_policy",
)
hidden_value, memory_value_out = self.create_recurrent_encoder(
hidden_streams[1],
self.memory_in[:, _half_point:],
self.sequence_length,
name="lstm_value",
)
self.memory_out = tf.concat([memory_policy_out, memory_value_out],
axis=1,
name="recurrent_out")
else:
hidden_policy = hidden_streams[0]
hidden_value = hidden_streams[1]
mu = tf.layers.dense(
hidden_policy,
self.act_size[0],
activation=None,
kernel_initializer=LearningModel.scaled_init(0.01),
reuse=tf.AUTO_REUSE,
)
self.log_sigma_sq = tf.get_variable(
"log_sigma_squared",
[self.act_size[0]],
dtype=tf.float32,
initializer=tf.zeros_initializer(),
)
sigma_sq = tf.exp(self.log_sigma_sq)
self.epsilon = tf.placeholder(shape=[None, self.act_size[0]],
dtype=tf.float32,
name="epsilon")
# Clip and scale output to ensure actions are always within [-1, 1] range.
self.output_pre = mu + tf.sqrt(sigma_sq) * self.epsilon
output_post = tf.clip_by_value(self.output_pre, -3, 3) / 3
self.output = tf.identity(output_post, name="action")
self.selected_actions = tf.stop_gradient(output_post)
# Compute probability of model output.
all_probs = (-0.5 * tf.square(tf.stop_gradient(self.output_pre) - mu) /
sigma_sq - 0.5 * tf.log(2.0 * np.pi) -
0.5 * self.log_sigma_sq)
self.all_log_probs = tf.identity(all_probs, name="action_probs")
self.entropy = 0.5 * tf.reduce_mean(
tf.log(2 * np.pi * np.e) + self.log_sigma_sq)
self.create_value_heads(self.stream_names, hidden_value)
self.all_old_log_probs = tf.placeholder(shape=[None, self.act_size[0]],
dtype=tf.float32,
name="old_probabilities")
# We keep these tensors the same name, but use new nodes to keep code parallelism with discrete control.
self.log_probs = tf.reduce_sum((tf.identity(self.all_log_probs)),
axis=1,
keepdims=True)
self.old_log_probs = tf.reduce_sum(
(tf.identity(self.all_old_log_probs)), axis=1, keepdims=True)
def create_dc_actor_critic(self, h_size: int, num_layers: int,
vis_encode_type: EncoderType) -> None:
"""
Creates Discrete control actor-critic model.
:param h_size: Size of hidden linear layers.
:param num_layers: Number of hidden linear layers.
"""
hidden_streams = self.create_observation_streams(
1, h_size, num_layers, vis_encode_type)
hidden = hidden_streams[0]
if self.use_recurrent:
self.prev_action = tf.placeholder(shape=[None,
len(self.act_size)],
dtype=tf.int32,
name="prev_action")
prev_action_oh = tf.concat(
[
tf.one_hot(self.prev_action[:, i], self.act_size[i])
for i in range(len(self.act_size))
],
axis=1,
)
hidden = tf.concat([hidden, prev_action_oh], axis=1)
self.memory_in = tf.placeholder(shape=[None, self.m_size],
dtype=tf.float32,
name="recurrent_in")
hidden, memory_out = self.create_recurrent_encoder(
hidden, self.memory_in, self.sequence_length)
self.memory_out = tf.identity(memory_out, name="recurrent_out")
policy_branches = []
for size in self.act_size:
policy_branches.append(
tf.layers.dense(
hidden,
size,
activation=None,
use_bias=False,
kernel_initializer=LearningModel.scaled_init(0.01),
))
self.all_log_probs = tf.concat(policy_branches,
axis=1,
name="action_probs")
self.action_masks = tf.placeholder(shape=[None,
sum(self.act_size)],
dtype=tf.float32,
name="action_masks")
output, _, normalized_logits = self.create_discrete_action_masking_layer(
self.all_log_probs, self.action_masks, self.act_size)
self.output = tf.identity(output)
self.normalized_logits = tf.identity(normalized_logits, name="action")
self.create_value_heads(self.stream_names, hidden)
self.action_holder = tf.placeholder(shape=[None,
len(policy_branches)],
dtype=tf.int32,
name="action_holder")
self.action_oh = tf.concat(
[
tf.one_hot(self.action_holder[:, i], self.act_size[i])
for i in range(len(self.act_size))
],
axis=1,
)
self.selected_actions = tf.stop_gradient(self.action_oh)
self.all_old_log_probs = tf.placeholder(
shape=[None, sum(self.act_size)],
dtype=tf.float32,
name="old_probabilities")
_, _, old_normalized_logits = self.create_discrete_action_masking_layer(
self.all_old_log_probs, self.action_masks, self.act_size)
action_idx = [0] + list(np.cumsum(self.act_size))
self.entropy = tf.reduce_sum(
(tf.stack(
[
tf.nn.softmax_cross_entropy_with_logits_v2(
labels=tf.nn.softmax(
self.all_log_probs[:,
action_idx[i]:action_idx[i +
1]]),
logits=self.all_log_probs[:,
action_idx[i]:action_idx[i +
1]],
) for i in range(len(self.act_size))
],
axis=1,
)),
axis=1,
)
self.log_probs = tf.reduce_sum(
(tf.stack(
[
-tf.nn.softmax_cross_entropy_with_logits_v2(
labels=self.action_oh[:,
action_idx[i]:action_idx[i + 1]],
logits=normalized_logits[:,
action_idx[i]:action_idx[i +
1]],
) for i in range(len(self.act_size))
],
axis=1,
)),
axis=1,
keepdims=True,
)
self.old_log_probs = tf.reduce_sum(
(tf.stack(
[
-tf.nn.softmax_cross_entropy_with_logits_v2(
labels=self.action_oh[:,
action_idx[i]:action_idx[i + 1]],
logits=old_normalized_logits[:, action_idx[i]:
action_idx[i + 1]],
) for i in range(len(self.act_size))
],
axis=1,
)),
axis=1,
keepdims=True,
)
def _create_dc_actor(self, encoded: tf.Tensor) -> None:
"""
Creates Discrete control actor-critic model.
:param h_size: Size of hidden linear layers.
:param num_layers: Number of hidden linear layers.
:param vis_encode_type: Type of visual encoder to use if visual input.
"""
if self.use_recurrent:
self.prev_action = tf.placeholder(shape=[None,
len(self.act_size)],
dtype=tf.int32,
name="prev_action")
prev_action_oh = tf.concat(
[
tf.one_hot(self.prev_action[:, i], self.act_size[i])
for i in range(len(self.act_size))
],
axis=1,
)
hidden_policy = tf.concat([encoded, prev_action_oh], axis=1)
self.memory_in = tf.placeholder(shape=[None, self.m_size],
dtype=tf.float32,
name="recurrent_in")
hidden_policy, memory_policy_out = ModelUtils.create_recurrent_encoder(
hidden_policy,
self.memory_in,
self.sequence_length_ph,
name="lstm_policy",
)
self.memory_out = tf.identity(memory_policy_out, "recurrent_out")
else:
hidden_policy = encoded
policy_branches = []
with tf.variable_scope("policy"):
for size in self.act_size:
policy_branches.append(
tf.layers.dense(
hidden_policy,
size,
activation=None,
use_bias=False,
kernel_initializer=ModelUtils.scaled_init(0.01),
))
raw_log_probs = tf.concat(policy_branches, axis=1, name="action_probs")
self.action_masks = tf.placeholder(shape=[None,
sum(self.act_size)],
dtype=tf.float32,
name="action_masks")
output, self.action_probs, normalized_logits = ModelUtils.create_discrete_action_masking_layer(
raw_log_probs, self.action_masks, self.act_size)
self.output = tf.identity(output)
self.all_log_probs = tf.identity(normalized_logits, name="action")
self.action_holder = tf.placeholder(shape=[None,
len(policy_branches)],
dtype=tf.int32,
name="action_holder")
self.action_oh = tf.concat(
[
tf.one_hot(self.action_holder[:, i], self.act_size[i])
for i in range(len(self.act_size))
],
axis=1,
)
self.selected_actions = tf.stop_gradient(self.action_oh)
action_idx = [0] + list(np.cumsum(self.act_size))
self.entropy = tf.reduce_sum(
(tf.stack(
[
tf.nn.softmax_cross_entropy_with_logits_v2(
labels=tf.nn.softmax(
self.all_log_probs[:,
action_idx[i]:action_idx[i +
1]]),
logits=self.all_log_probs[:,
action_idx[i]:action_idx[i +
1]],
) for i in range(len(self.act_size))
],
axis=1,
)),
axis=1,
)
self.log_probs = tf.reduce_sum(
(tf.stack(
[
-tf.nn.softmax_cross_entropy_with_logits_v2(
labels=self.action_oh[:,
action_idx[i]:action_idx[i + 1]],
logits=normalized_logits[:,
action_idx[i]:action_idx[i +
1]],
) for i in range(len(self.act_size))
],
axis=1,
)),
axis=1,
keepdims=True,
)
def _create_cc_actor(
self,
encoded: tf.Tensor,
tanh_squash: bool = False,
reparameterize: bool = False,
condition_sigma_on_obs: bool = True,
) -> None:
"""
Creates Continuous control actor-critic model.
:param h_size: Size of hidden linear layers.
:param num_layers: Number of hidden linear layers.
:param vis_encode_type: Type of visual encoder to use if visual input.
:param tanh_squash: Whether to use a tanh function, or a clipped output.
:param reparameterize: Whether we are using the resampling trick to update the policy.
"""
if self.use_recurrent:
self.memory_in = tf.placeholder(shape=[None, self.m_size],
dtype=tf.float32,
name="recurrent_in")
hidden_policy, memory_policy_out = ModelUtils.create_recurrent_encoder(
encoded,
self.memory_in,
self.sequence_length_ph,
name="lstm_policy")
self.memory_out = tf.identity(memory_policy_out,
name="recurrent_out")
else:
hidden_policy = encoded
with tf.variable_scope("policy"):
mu = tf.layers.dense(
hidden_policy,
self.act_size[0],
activation=None,
name="mu",
kernel_initializer=ModelUtils.scaled_init(0.01),
reuse=tf.AUTO_REUSE,
)
# Policy-dependent log_sigma
if condition_sigma_on_obs:
log_sigma = tf.layers.dense(
hidden_policy,
self.act_size[0],
activation=None,
name="log_sigma",
kernel_initializer=ModelUtils.scaled_init(0.01),
)
else:
log_sigma = tf.get_variable(
"log_sigma",
[self.act_size[0]],
dtype=tf.float32,
initializer=tf.zeros_initializer(),
)
log_sigma = tf.clip_by_value(log_sigma, self.log_std_min,
self.log_std_max)
sigma = tf.exp(log_sigma)
epsilon = tf.random_normal(tf.shape(mu))
sampled_policy = mu + sigma * epsilon
# Stop gradient if we're not doing the resampling trick
if not reparameterize:
sampled_policy_probs = tf.stop_gradient(sampled_policy)
else:
sampled_policy_probs = sampled_policy
# Compute probability of model output.
_gauss_pre = -0.5 * (
((sampled_policy_probs - mu) /
(sigma + EPSILON))**2 + 2 * log_sigma + np.log(2 * np.pi))
all_probs = _gauss_pre
all_probs = tf.reduce_sum(_gauss_pre, axis=1, keepdims=True)
if tanh_squash:
self.output_pre = tf.tanh(sampled_policy)
# Squash correction
all_probs -= tf.reduce_sum(tf.log(1 - self.output_pre**2 +
EPSILON),
axis=1,
keepdims=True)
self.output = tf.identity(self.output_pre, name="action")
else:
self.output_pre = sampled_policy
# Clip and scale output to ensure actions are always within [-1, 1] range.
output_post = tf.clip_by_value(self.output_pre, -3, 3) / 3
self.output = tf.identity(output_post, name="action")
self.selected_actions = tf.stop_gradient(self.output)
self.all_log_probs = tf.identity(all_probs, name="action_probs")
single_dim_entropy = 0.5 * tf.reduce_mean(
tf.log(2 * np.pi * np.e) + 2 * log_sigma)
# Make entropy the right shape
self.entropy = tf.ones_like(tf.reshape(mu[:, 0],
[-1])) * single_dim_entropy
# We keep these tensors the same name, but use new nodes to keep code parallelism with discrete control.
self.log_probs = tf.reduce_sum((tf.identity(self.all_log_probs)),
axis=1,
keepdims=True)
self.action_holder = tf.placeholder(shape=[None, self.act_size[0]],
dtype=tf.float32,
name="action_holder")
