duel.py 文件源码

def createLayers():
    x = Input(shape=env.observation_space.shape)
    if args.batch_norm:
        h = BatchNormalization()(x)
    else:
        h = x
    for i in xrange(args.layers):
        h = Dense(args.hidden_size, activation=args.activation)(h)
        if args.batch_norm and i != args.layers - 1:
            h = BatchNormalization()(h)
    y = Dense(env.action_space.n + 1)(h)
    if args.advantage == 'avg':
        z = Lambda(lambda a: K.expand_dims(a[:, 0], dim=-1) + a[:, 1:] - K.mean(a[:, 1:], keepdims=True), output_shape=(env.action_space.n,))(y)
    elif args.advantage == 'max':
        z = Lambda(lambda a: K.expand_dims(a[:, 0], dim=-1) + a[:, 1:] - K.max(a[:, 1:], keepdims=True), output_shape=(env.action_space.n,))(y)
    elif args.advantage == 'naive':
        z = Lambda(lambda a: K.expand_dims(a[:, 0], dim=-1) + a[:, 1:], output_shape=(env.action_space.n,))(y)
    else:
        assert False

    return x, z