graphenv.examples.hallway.hallway_model.HallwayModel

class HallwayModel(*args, hidden_dim=1, **kwargs)[source]

Bases: graphenv.examples.hallway.hallway_model.BaseHallwayModel, ray.rllib.models.tf.tf_modelv2.TFModelV2

Initializes a TFModelV2 instance.

Here is an example implementation for a subclass MyModelClass(TFModelV2):

def __init__(self, *args, **kwargs):
    super(MyModelClass, self).__init__(*args, **kwargs)
    input_layer = tf.keras.layers.Input(...)
    hidden_layer = tf.keras.layers.Dense(...)(input_layer)
    output_layer = tf.keras.layers.Dense(...)(hidden_layer)
    value_layer = tf.keras.layers.Dense(...)(hidden_layer)
    self.base_model = tf.keras.Model(
        input_layer, [output_layer, value_layer])

Methods

`context`	Returns a contextmanager for the current TF graph.
`custom_loss`	Override to customize the loss function used to optimize this model.
`forward`	Tensorflow/Keras style forward method.
`forward_vertex`	Forward function computing the evaluation of vertex observations.
`from_batch`
`get_initial_state`	Get the initial recurrent state values for the model.
`import_from_h5`	Imports weights from an h5 file.
`is_time_major`	If True, data for calling this ModelV2 must be in time-major format.
`last_output`	Returns the last output returned from calling the model.
`metrics`	Override to return custom metrics from your model.
`register_variables`	Register the given list of variables with this model.
`trainable_variables`	Returns the list of trainable variables for this model.
`update_ops`	Return the list of update ops for this model.
`value_function`	returns A tensor of current state values.
`variables`	Returns the list (or a dict) of variables for this model.

Parameters: hidden_dim (int) –