def _create_encoder(
self,
visual_in: List[tf.Tensor],
vector_in: tf.Tensor,
h_size: int,
num_layers: int,
vis_encode_type: EncoderType,
) -> tf.Tensor:
"""
Creates an encoder for visual and vector observations.
: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.
:return: The hidden layer (tf.Tensor) after the encoder.
"""
with tf.variable_scope("policy"):
encoded = ModelUtils.create_observation_streams(
self.visual_in,
self.processed_vector_in,
1,
h_size,
num_layers,
vis_encode_type,
)[0]
return encoded
def _create_dc_critic(self, h_size: int, num_layers: int,
vis_encode_type: EncoderType) -> None:
"""
Creates Discrete 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",
)
_, _, old_normalized_logits = ModelUtils.create_discrete_action_masking_layer(
self.all_old_log_probs, self.policy.action_masks,
self.policy.act_size)
action_idx = [0] + list(np.cumsum(self.policy.act_size))
self.old_log_probs = tf.reduce_sum(
(tf.stack(
[
-tf.nn.softmax_cross_entropy_with_logits_v2(
labels=self.policy.
selected_actions[:, action_idx[i]:action_idx[i + 1]],
logits=old_normalized_logits[:, action_idx[i]:
action_idx[i + 1]],
) for i in range(len(self.policy.act_size))
],
axis=1,
)),
axis=1,
keepdims=True,
)
def _create_observation_in(self, vis_encode_type):
"""
Creates the observation inputs, and a CNN if needed,
:param vis_encode_type: Type of CNN encoder.
:param share_ac_cnn: Whether or not to share the actor and critic CNNs.
:return A tuple of (hidden_policy, hidden_critic). We don't save it to self since they're used
once and thrown away.
"""
with tf.variable_scope(POLICY_SCOPE):
hidden_streams = ModelUtils.create_observation_streams(
self.policy.visual_in,
self.policy.processed_vector_in,
1,
self.h_size,
0,
vis_encode_type=vis_encode_type,
stream_scopes=["critic/value/"],
)
hidden_critic = hidden_streams[0]
return hidden_critic
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 __init__(
self,
policy,
m_size=None,
h_size=128,
normalize=False,
use_recurrent=False,
num_layers=2,
stream_names=None,
vis_encode_type=EncoderType.SIMPLE,
):
super().__init__(
policy,
m_size,
h_size,
normalize,
use_recurrent,
num_layers,
stream_names,
vis_encode_type,
)
with tf.variable_scope(TARGET_SCOPE):
self.visual_in = ModelUtils.create_visual_input_placeholders(
policy.brain.camera_resolutions
)
self.vector_in = ModelUtils.create_vector_input(policy.vec_obs_size)
if self.policy.normalize:
normalization_tensors = ModelUtils.create_normalizer(self.vector_in)
self.update_normalization_op = normalization_tensors.update_op
self.normalization_steps = normalization_tensors.steps
self.running_mean = normalization_tensors.running_mean
self.running_variance = normalization_tensors.running_variance
self.processed_vector_in = ModelUtils.normalize_vector_obs(
self.vector_in,
self.running_mean,
self.running_variance,
self.normalization_steps,
)
else:
self.processed_vector_in = self.vector_in
self.update_normalization_op = None
if self.policy.use_recurrent:
self.memory_in = tf.placeholder(
shape=[None, m_size], dtype=tf.float32, name="target_recurrent_in"
)
self.value_memory_in = self.memory_in
hidden_streams = ModelUtils.create_observation_streams(
self.visual_in,
self.processed_vector_in,
1,
self.h_size,
0,
vis_encode_type=vis_encode_type,
stream_scopes=["critic/value/"],
)
if self.policy.use_continuous_act:
self._create_cc_critic(hidden_streams[0], TARGET_SCOPE, create_qs=False)
else:
self._create_dc_critic(hidden_streams[0], TARGET_SCOPE, create_qs=False)
if self.use_recurrent:
self.memory_out = tf.concat(
self.value_memory_out, axis=1
) # Needed for Barracuda to work
