categorical_mlp_policy.py 文件源码

def __init__(
            self,
            env_spec,
            hidden_sizes=(32, 32),
            hidden_nonlinearity=NL.tanh,
            num_seq_inputs=1,
            prob_network=None,
    ):
        """
        :param env_spec: A spec for the mdp.
        :param hidden_sizes: list of sizes for the fully connected hidden layers
        :param hidden_nonlinearity: nonlinearity used for each hidden layer
        :param prob_network: manually specified network for this policy, other network params
        are ignored
        :return:
        """
        Serializable.quick_init(self, locals())

        assert isinstance(env_spec.action_space, Discrete)

        if prob_network is None:
            prob_network = MLP(
                input_shape=(env_spec.observation_space.flat_dim * num_seq_inputs,),
                output_dim=env_spec.action_space.n,
                hidden_sizes=hidden_sizes,
                hidden_nonlinearity=hidden_nonlinearity,
                output_nonlinearity=NL.softmax,
            )

        self._l_prob = prob_network.output_layer
        self._l_obs = prob_network.input_layer
        self._f_prob = ext.compile_function([prob_network.input_layer.input_var], L.get_output(
            prob_network.output_layer))

        self._dist = Categorical(env_spec.action_space.n)

        super(CategoricalMLPPolicy, self).__init__(env_spec)
        LasagnePowered.__init__(self, [prob_network.output_layer])