deterministic_mlp_policy.py 文件源码

def __init__(
            self,
            env_spec,
            hidden_sizes=(32, 32),
            hidden_nonlinearity=NL.rectify,
            hidden_W_init=LI.HeUniform(),
            hidden_b_init=LI.Constant(0.),
            output_nonlinearity=NL.tanh,
            output_W_init=LI.Uniform(-3e-3, 3e-3),
            output_b_init=LI.Uniform(-3e-3, 3e-3),
            bn=False):
        Serializable.quick_init(self, locals())

        l_obs = L.InputLayer(shape=(None, env_spec.observation_space.flat_dim))

        l_hidden = l_obs
        if bn:
            l_hidden = batch_norm(l_hidden)

        for idx, size in enumerate(hidden_sizes):
            l_hidden = L.DenseLayer(
                l_hidden,
                num_units=size,
                W=hidden_W_init,
                b=hidden_b_init,
                nonlinearity=hidden_nonlinearity,
                name="h%d" % idx
            )
            if bn:
                l_hidden = batch_norm(l_hidden)

        l_output = L.DenseLayer(
            l_hidden,
            num_units=env_spec.action_space.flat_dim,
            W=output_W_init,
            b=output_b_init,
            nonlinearity=output_nonlinearity,
            name="output"
        )

        # Note the deterministic=True argument. It makes sure that when getting
        # actions from single observations, we do not update params in the
        # batch normalization layers

        action_var = L.get_output(l_output, deterministic=True)
        self._output_layer = l_output

        self._f_actions = ext.compile_function([l_obs.input_var], action_var)

        super(DeterministicMLPPolicy, self).__init__(env_spec)
        LasagnePowered.__init__(self, [l_output])