def _build_layers_v2(self, input_dict, num_outputs, options):
self.obs_in = input_dict["obs"]
with tf.variable_scope("shared", reuse=tf.AUTO_REUSE):
self.fcnet = FullyConnectedNetwork(input_dict, self.obs_space,
self.action_space, num_outputs,
options)
return self.fcnet.outputs, self.fcnet.last_layer
def _build_layers_v2(self, input_dict, num_outputs, options):
self.fcnet = FullyConnectedNetwork(input_dict, self.obs_space,
self.action_space, num_outputs,
options)
feature_out = tf.py_function(self.forward_eager,
[self.fcnet.last_layer], tf.float32)
with tf.control_dependencies([feature_out]):
return tf.identity(self.fcnet.outputs), feature_out
def _get_model(input_dict, obs_space, action_space, num_outputs, options,
state_in, seq_lens):
if options.get("custom_model"):
model = options["custom_model"]
logger.debug("Using custom model {}".format(model))
return _global_registry.get(RLLIB_MODEL, model)(input_dict,
obs_space,
action_space,
num_outputs,
options,
state_in=state_in,
seq_lens=seq_lens)
obs_rank = len(input_dict["obs"].shape) - 1 # drops batch dim
if obs_rank > 2:
return VisionNetwork(input_dict, obs_space, action_space,
num_outputs, options)
return FullyConnectedNetwork(input_dict, obs_space, action_space,
num_outputs, options)
