(
    body=None,
    normalizer=None,
    batch_axes=None,
    mode='train',
    n_actions=2,
    head_init_range=None,
)
return tl.Serial(
      _Batch(normalizer(mode=mode), batch_axes),
      _Batch(body(mode=mode), batch_axes),
      tl.Dense(n_actions, **head_kwargs),
  )
The network takes as input an observation and outputs values of actions.
The network takes as input an observation and outputs values of actions.
The network takes as input an observation and outputs values of actions .
def Quality(
    body=None,
    normalizer=None,
    batch_axes=None,
    mode='train',
    n_actions=2,
    head_init_range=None,
):
  """The network takes as input an observation and outputs values of actions."""

  if body is None:
    body = lambda mode: []
  if normalizer is None:
    normalizer = lambda mode: []

  head_kwargs = {}
  if head_init_range is not None:
    head_kwargs['kernel_initializer'] = tl.RandomUniformInitializer(
        lim=head_init_range
    )

  return tl.Serial(
      _Batch(normalizer(mode=mode), batch_axes),
      _Batch(body(mode=mode), batch_axes),
      tl.Dense(n_actions, **head_kwargs),
  )
def Quality ( body = None , normalizer = None , batch_axes = None , mode = 'train' , n_actions = 2 , head_init_range = None , ) : if body is None : body = lambda mode : [ ] if normalizer is None : normalizer = lambda mode : [ ] head_kwargs = { } if head_init_range is not None : head_kwargs [ 'kernel_initializer' ] = tl . RandomUniformInitializer ( lim = head_init_range ) return tl . Serial ( _Batch ( normalizer ( mode = mode ) , batch_axes ) , _Batch ( body ( mode = mode ) , batch_axes ) , tl . Dense ( n_actions , * * head_kwargs ) , )