test_continuous.py 文件源码

def test_ddpg():
    # TODO: replace this with a simpler environment where we can actually test if it finds a solution
    env = gym.make('Pendulum-v0')
    np.random.seed(123)
    env.seed(123)
    random.seed(123)
    nb_actions = env.action_space.shape[0]

    actor = Sequential()
    actor.add(Flatten(input_shape=(1,) + env.observation_space.shape))
    actor.add(Dense(16))
    actor.add(Activation('relu'))
    actor.add(Dense(nb_actions))
    actor.add(Activation('linear'))

    action_input = Input(shape=(nb_actions,), name='action_input')
    observation_input = Input(shape=(1,) + env.observation_space.shape, name='observation_input')
    flattened_observation = Flatten()(observation_input)
    x = Concatenate()([action_input, flattened_observation])
    x = Dense(16)(x)
    x = Activation('relu')(x)
    x = Dense(1)(x)
    x = Activation('linear')(x)
    critic = Model(inputs=[action_input, observation_input], outputs=x)

    memory = SequentialMemory(limit=1000, window_length=1)
    random_process = OrnsteinUhlenbeckProcess(theta=.15, mu=0., sigma=.3)
    agent = DDPGAgent(nb_actions=nb_actions, actor=actor, critic=critic, critic_action_input=action_input,
                      memory=memory, nb_steps_warmup_critic=50, nb_steps_warmup_actor=50,
                      random_process=random_process, gamma=.99, target_model_update=1e-3)
    agent.compile([Adam(lr=1e-3), Adam(lr=1e-3)])

    agent.fit(env, nb_steps=400, visualize=False, verbose=0, nb_max_episode_steps=100)
    h = agent.test(env, nb_episodes=2, visualize=False, nb_max_episode_steps=100)
    # TODO: evaluate history