def create_discrete_action_masking_layer(all_logits, action_masks,
action_size):
"""
Creates a masking layer for the discrete actions
:param all_logits: The concatenated unnormalized action probabilities for all branches
:param action_masks: The mask for the logits. Must be of dimension [None x total_number_of_action]
:param action_size: A list containing the number of possible actions for each branch
:return: The action output dimension [batch_size, num_branches], the concatenated
normalized probs (after softmax)
and the concatenated normalized log probs
"""
action_idx = [0] + list(np.cumsum(action_size))
branches_logits = [
all_logits[:, action_idx[i]:action_idx[i + 1]]
for i in range(len(action_size))
]
branch_masks = [
action_masks[:, action_idx[i]:action_idx[i + 1]]
for i in range(len(action_size))
]
raw_probs = [
tf.multiply(
tf.nn.softmax(branches_logits[k]) + EPSILON, branch_masks[k])
for k in range(len(action_size))
]
normalized_probs = [
tf.divide(raw_probs[k],
tf.reduce_sum(raw_probs[k], axis=1, keepdims=True))
for k in range(len(action_size))
]
output = tf.concat(
[
tf.multinomial(tf.log(normalized_probs[k] + EPSILON), 1)
for k in range(len(action_size))
],
axis=1,
)
return (
output,
tf.concat([normalized_probs[k] for k in range(len(action_size))],
axis=1),
tf.concat(
[
tf.log(normalized_probs[k] + EPSILON)
for k in range(len(action_size))
],
axis=1,
),
)
def create_discrete_action_masking_layer(
branches_logits: List[tf.Tensor],
action_masks: tf.Tensor,
action_size: List[int],
) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]:
"""
Creates a masking layer for the discrete actions
:param branches_logits: A List of the unnormalized action probabilities for each branch
:param action_masks: The mask for the logits. Must be of dimension [None x total_number_of_action]
:param action_size: A list containing the number of possible actions for each branch
:return: The action output dimension [batch_size, num_branches], the concatenated
normalized probs (after softmax)
and the concatenated normalized log probs
"""
branch_masks = ModelUtils.break_into_branches(action_masks,
action_size)
raw_probs = [
tf.multiply(
tf.nn.softmax(branches_logits[k]) + EPSILON, branch_masks[k])
for k in range(len(action_size))
]
normalized_probs = [
tf.divide(raw_probs[k],
tf.reduce_sum(raw_probs[k], axis=1, keepdims=True))
for k in range(len(action_size))
]
output = tf.concat(
[
tf.multinomial(tf.log(normalized_probs[k] + EPSILON), 1)
for k in range(len(action_size))
],
axis=1,
)
return (
output,
tf.concat([normalized_probs[k] for k in range(len(action_size))],
axis=1),
tf.concat(
[
tf.log(normalized_probs[k] + EPSILON)
for k in range(len(action_size))
],
axis=1,
),
)
def swish(input_activation: tf.Tensor) -> tf.Tensor:
"""Swish activation function. For more info: https://arxiv.org/abs/1710.05941"""
return tf.multiply(input_activation, tf.nn.sigmoid(input_activation))
