(policy_logits, actions)
return -tf.nn.sparse_softmax_cross_entropy_with_logits(
      logits=policy_logits, labels=actions)
Computes action log-probs from policy logits and actions.

  In the notation used throughout documentation and comments, T refers to the
  time dimension ranging from 0 to T-1. B refers to the batch size and
  NUM_ACTIONS refers to the number of actions.

  Args:
    policy_logits: A float32 tensor of shape [T, B, NUM_ACTIONS] with
      un-normalized log-probabilities parameterizing a softmax policy.
    actions: An int32 tensor of shape [T, B] with actions.

  Returns:
    A float32 tensor of shape [T, B] corresponding to the sampling log
    probability of the chosen action w.r.t. the policy.
Computes action log-probs from policy logits and actions.
Computes action log - probs from policy logits and actions .
def log_probs_from_logits_and_actions(policy_logits, actions):
  """Computes action log-probs from policy logits and actions.

  In the notation used throughout documentation and comments, T refers to the
  time dimension ranging from 0 to T-1. B refers to the batch size and
  NUM_ACTIONS refers to the number of actions.

  Args:
    policy_logits: A float32 tensor of shape [T, B, NUM_ACTIONS] with
      un-normalized log-probabilities parameterizing a softmax policy.
    actions: An int32 tensor of shape [T, B] with actions.

  Returns:
    A float32 tensor of shape [T, B] corresponding to the sampling log
    probability of the chosen action w.r.t. the policy.
  """
  policy_logits = tf.convert_to_tensor(policy_logits, dtype=tf.float32)
  actions = tf.convert_to_tensor(actions, dtype=tf.int32)

  policy_logits.shape.assert_has_rank(3)
  actions.shape.assert_has_rank(2)

  return -tf.nn.sparse_softmax_cross_entropy_with_logits(
      logits=policy_logits, labels=actions)
def log_probs_from_logits_and_actions ( policy_logits , actions ) : policy_logits = tf . convert_to_tensor ( policy_logits , dtype = tf . float32 ) actions = tf . convert_to_tensor ( actions , dtype = tf . int32 ) policy_logits . shape . assert_has_rank ( 3 ) actions . shape . assert_has_rank ( 2 ) return - tf . nn . sparse_softmax_cross_entropy_with_logits ( logits = policy_logits , labels = actions )