dqn_m.py 文件源码

def delling_network():
    """ Architecture according to Duelling DQN:
    https://arxiv.org/abs/1511.06581
    """

    @tt.model(tracker=tf.train.ExponentialMovingAverage(1 - .0005),    # TODO: replace with original weight freeze
                         optimizer=tf.train.RMSPropOptimizer(6.25e-5, .95, .95, .01))
    def q_network(x):
        x /= 255
        x = layers.conv2d(x, 32, 8, 4)
        x = layers.conv2d(x, 64, 4, 2)
        x = layers.conv2d(x, 64, 3, 1)
        x = layers.flatten(x)

        xv = layers.fully_connected(x, 512)
        val = layers.fully_connected(xv, 1, activation_fn=None)
        # val = tf.squeeze(val, 1)

        xa = layers.fully_connected(x, 512)
        adv = layers.fully_connected(xa, env.action_space.n, activation_fn=None)

        q = val + adv - tf.reduce_mean(adv, axis=1, keep_dims=True)
        q = tf.identity(q, name='Q')
        return q


# Tests