def test_create_input_placeholders(num_vector, num_visual):
vec_size = 8
name_prefix = "test123"
bspec = create_behavior_spec(num_visual, num_vector, vec_size)
vec_in, vis_in = ModelUtils.create_input_placeholders(
bspec.observation_shapes, name_prefix=name_prefix)
assert isinstance(vis_in, list)
assert len(vis_in) == num_visual
assert isinstance(vec_in, tf.Tensor)
assert vec_in.get_shape().as_list()[1] == num_vector * 8
# Check names contain prefix and vis shapes are correct
for _vis in vis_in:
assert _vis.get_shape().as_list() == [None, 84, 84, 3]
assert _vis.name.startswith(name_prefix)
assert vec_in.name.startswith(name_prefix)
def create_curiosity_encoders(self) -> Tuple[tf.Tensor, tf.Tensor]:
"""
Creates state encoders for current and future observations.
Used for implementation of Curiosity-driven Exploration by Self-supervised Prediction
See https://arxiv.org/abs/1705.05363 for more details.
:return: current and future state encoder tensors.
"""
encoded_state_list = []
encoded_next_state_list = []
# Create input ops for next (t+1) visual observations.
self.next_vector_in, self.next_visual_in = ModelUtils.create_input_placeholders(
self.policy.behavior_spec.observation_shapes,
name_prefix="curiosity_next_")
if self.next_visual_in:
visual_encoders = []
next_visual_encoders = []
for i, (vis_in, next_vis_in) in enumerate(
zip(self.policy.visual_in, self.next_visual_in)):
# Create the encoder ops for current and next visual input.
# Note that these encoders are siamese.
encoded_visual = ModelUtils.create_visual_observation_encoder(
vis_in,
self.encoding_size,
ModelUtils.swish,
1,
"curiosity_stream_{}_visual_obs_encoder".format(i),
False,
)
encoded_next_visual = ModelUtils.create_visual_observation_encoder(
next_vis_in,
self.encoding_size,
ModelUtils.swish,
1,
"curiosity_stream_{}_visual_obs_encoder".format(i),
True,
)
visual_encoders.append(encoded_visual)
next_visual_encoders.append(encoded_next_visual)
hidden_visual = tf.concat(visual_encoders, axis=1)
hidden_next_visual = tf.concat(next_visual_encoders, axis=1)
encoded_state_list.append(hidden_visual)
encoded_next_state_list.append(hidden_next_visual)
if self.policy.vec_obs_size > 0:
encoded_vector_obs = ModelUtils.create_vector_observation_encoder(
self.policy.vector_in,
self.encoding_size,
ModelUtils.swish,
2,
"curiosity_vector_obs_encoder",
False,
)
encoded_next_vector_obs = ModelUtils.create_vector_observation_encoder(
self.next_vector_in,
self.encoding_size,
ModelUtils.swish,
2,
"curiosity_vector_obs_encoder",
True,
)
encoded_state_list.append(encoded_vector_obs)
encoded_next_state_list.append(encoded_next_vector_obs)
encoded_state = tf.concat(encoded_state_list, axis=1)
encoded_next_state = tf.concat(encoded_next_state_list, axis=1)
return encoded_state, encoded_next_state
def create_input_placeholders(self):
with self.graph.as_default():
(
self.global_step,
self.increment_step_op,
self.steps_to_increment,
) = ModelUtils.create_global_steps()
self.vector_in, self.visual_in = ModelUtils.create_input_placeholders(
self.behavior_spec.observation_shapes)
if self.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
self.batch_size_ph = tf.placeholder(shape=None,
dtype=tf.int32,
name="batch_size")
self.sequence_length_ph = tf.placeholder(shape=None,
dtype=tf.int32,
name="sequence_length")
self.mask_input = tf.placeholder(shape=[None],
dtype=tf.float32,
name="masks")
# Only needed for PPO, but needed for BC module
self.epsilon = tf.placeholder(shape=[None, self.act_size[0]],
dtype=tf.float32,
name="epsilon")
self.mask = tf.cast(self.mask_input, tf.int32)
tf.Variable(
int(self.behavior_spec.is_action_continuous()),
name="is_continuous_control",
trainable=False,
dtype=tf.int32,
)
int_version = TFPolicy._convert_version_string(__version__)
major_ver_t = tf.Variable(
int_version[0],
name="trainer_major_version",
trainable=False,
dtype=tf.int32,
)
minor_ver_t = tf.Variable(
int_version[1],
name="trainer_minor_version",
trainable=False,
dtype=tf.int32,
)
patch_ver_t = tf.Variable(
int_version[2],
name="trainer_patch_version",
trainable=False,
dtype=tf.int32,
)
self.version_tensors = (major_ver_t, minor_ver_t, patch_ver_t)
tf.Variable(
MODEL_FORMAT_VERSION,
name="version_number",
trainable=False,
dtype=tf.int32,
)
tf.Variable(self.m_size,
name="memory_size",
trainable=False,
dtype=tf.int32)
if self.behavior_spec.is_action_continuous():
tf.Variable(
self.act_size[0],
name="action_output_shape",
trainable=False,
dtype=tf.int32,
)
else:
tf.Variable(
sum(self.act_size),
name="action_output_shape",
trainable=False,
dtype=tf.int32,
)
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,
"gail_stream_{}_visual_obs_encoder".format(i),
False,
)
encoded_expert_visual = ModelUtils.create_visual_observation_encoder(
exp_vis_in,
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 __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.vector_in, self.visual_in = ModelUtils.create_input_placeholders(
self.policy.behavior_spec.observation_shapes
)
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
