Source code for

from ray.rllib.models.model import Model
from import normc_initializer, get_activation_fn
from ray.rllib.utils.annotations import override
from ray.rllib.utils import try_import_tf

tf = try_import_tf()

# Deprecated: see as an alternative models/tf/
[docs]class FullyConnectedNetwork(Model): """Generic fully connected network.""" @override(Model) def _build_layers(self, inputs, num_outputs, options): """Process the flattened inputs. Note that dict inputs will be flattened into a vector. To define a model that processes the components separately, use _build_layers_v2(). """ hiddens = options.get("fcnet_hiddens") activation = get_activation_fn(options.get("fcnet_activation")) if len(inputs.shape) > 2: inputs = tf.layers.flatten(inputs) with tf.name_scope("fc_net"): i = 1 last_layer = inputs for size in hiddens: # skip final linear layer if options.get("no_final_linear") and i == len(hiddens): output = tf.layers.dense( last_layer, num_outputs, kernel_initializer=normc_initializer(1.0), activation=activation, name="fc_out") return output, output label = "fc{}".format(i) last_layer = tf.layers.dense( last_layer, size, kernel_initializer=normc_initializer(1.0), activation=activation, name=label) i += 1 output = tf.layers.dense( last_layer, num_outputs, kernel_initializer=normc_initializer(0.01), activation=None, name="fc_out") return output, last_layer