【百度AI强化学习系列】四、基于策略梯度求解RL（用PG解决Pong）

Policy Gradient

在强化学习中，有两大类方法，一种基于值（Value-based），一种基于策略（Policy-based），Policy Gradient属于后者。PG不需要想DQN那样对Q值进行拟合，它直接拟合出Agent下一步的动作，这就和端到端的神经网络一样，没有中间环节，如下图所示。

公式推导

期望回报

在一个Episode中。Agent的活动用一个序列 $\tau =\left\{s_1,a_1,s_2,a_2,...,s_T,a_T\right\}$表示，其中$s_T$代表$T$步时的状态，$s_T$代表$T$步时的action。

${\pi }_{\theta }=(a_T|s_T)$是通过网络预测出Agent所做动作的概率。

优化目标与策略梯度

③式中$\rho (s_1)$和$\rho (s_{t+1}|s_t,a_{t}0)$在对$\theta$求导时会被约去，所以得到了④式。

项目练习（PG解决Pong）

课程与练习都是由百度AI强化学习7日打卡营提供（课程链接）

实战要求

分数从-21开始逐渐上涨收敛，Test reward的分数可以收敛到0分以上（说明打败对方了），越高越好。

网络结构

在撰写这篇文章的时候我还在疯狂的调试，参考过其他的解决方案，估计大部分的方案都是经过好几轮的训练才收敛到比较好的结果，我想继续尝试，看看用什么办法可以加快收敛速度。到时候再把自己的代码放出来。下面我贴出老师的网络结构：

class Model(parl.Model):
    def __init__(self, act_dim):
        act_dim = act_dim
        hid1_size = 256
        hid2_size = 64

        self.fc1 = layers.fc(size=hid1_size, act='relu')
        self.fc2 = layers.fc(size=hid2_size, act='relu')
        self.fc3 = layers.fc(size=act_dim, act='softmax')

    def forward(self, obs):
        h1 = self.fc1(obs)
        h2 = self.fc2(h1)
        out = self.fc3(h2)
        return out

Agent：

class Agent(parl.Agent):
    def __init__(self, algorithm, obs_dim, act_dim):
        self.obs_dim = obs_dim
        self.act_dim = act_dim
        super(Agent, self).__init__(algorithm)

    def build_program(self):
        self.pred_program = fluid.Program()
        self.learn_program = fluid.Program()

        with fluid.program_guard(self.pred_program):  # 搭建计算图用于 预测动作，定义输入输出变量
            obs = layers.data(
                name='obs', shape=[self.obs_dim], dtype='float32')
            self.act_prob = self.alg.predict(obs)

        with fluid.program_guard(
                self.learn_program):  # 搭建计算图用于 更新policy网络，定义输入输出变量
            obs = layers.data(
                name='obs', shape=[self.obs_dim], dtype='float32')
            act = layers.data(name='act', shape=[1], dtype='int64')
            reward = layers.data(name='reward', shape=[], dtype='float32')
            self.cost = self.alg.learn(obs, act, reward)

    def sample(self, obs):
        obs = np.expand_dims(obs, axis=0)  # 增加一维维度
        act_prob = self.fluid_executor.run(
            self.pred_program,
            feed={'obs': obs.astype('float32')},
            fetch_list=[self.act_prob])[0]
        act_prob = np.squeeze(act_prob, axis=0)  # 减少一维维度
        act = np.random.choice(range(self.act_dim), p=act_prob)  # 根据动作概率选取动作
        return act

    def predict(self, obs):
        obs = np.expand_dims(obs, axis=0)
        act_prob = self.fluid_executor.run(
            self.pred_program,
            feed={'obs': obs.astype('float32')},
            fetch_list=[self.act_prob])[0]
        act_prob = np.squeeze(act_prob, axis=0)
        act = np.argmax(act_prob)  # 根据动作概率选择概率最高的动作
        return act

    def learn(self, obs, act, reward):
        act = np.expand_dims(act, axis=-1)
        feed = {
            'obs': obs.astype('float32'),
            'act': act.astype('int64'),
            'reward': reward.astype('float32')
        }
        cost = self.fluid_executor.run(
            self.learn_program, feed=feed, fetch_list=[self.cost])[0]
        return cost

训练设置：

def run_episode(env, agent):
    obs_list, action_list, reward_list = [], [], []
    obs = env.reset()
    while True:
        obs = preprocess(obs)  # from shape (210, 160, 3) to (100800,)
        obs_list.append(obs)
        action = agent.sample(obs)
        action_list.append(action)

        obs, reward, done, info = env.step(action)
        reward_list.append(reward)

        if done:
            break
    return obs_list, action_list, reward_list


# 评估 agent, 跑 5 个episode，总reward求平均
def evaluate(env, agent, render=False):
    eval_reward = []
    for i in range(5):
        obs = env.reset()
        episode_reward = 0
        while True:
            obs = preprocess(obs)  # from shape (210, 160, 3) to (100800,)
            action = agent.predict(obs)
            obs, reward, isOver, _ = env.step(action)
            episode_reward += reward
            if render:
                env.render()
            if isOver:
                break
        eval_reward.append(episode_reward)
    return np.mean(eval_reward)


def preprocess(image):
    """ 预处理 210x160x3 uint8 frame into 6400 (80x80) 1维 float vector """
    image = image[35:195]  # 裁剪
    image = image[::2, ::2, 0]  # 下采样，缩放2倍
    image[image == 144] = 0  # 擦除背景 (background type 1)
    image[image == 109] = 0  # 擦除背景 (background type 2)
    image[image != 0] = 1  # 转为灰度图，除了黑色外其他都是白色
    return image.astype(np.float).ravel()


def calc_reward_to_go(reward_list, gamma=0.99):
    """calculate discounted reward"""
    reward_arr = np.array(reward_list)
    for i in range(len(reward_arr) - 2, -1, -1):
        # G_t = r_t + γ·r_t+1 + ... = r_t + γ·G_t+1
        reward_arr[i] += gamma * reward_arr[i + 1]
    # normalize episode rewards
    reward_arr -= np.mean(reward_arr)
    reward_arr /= np.std(reward_arr)
    return reward_arr


def main():
    env = gym.make('Pong-v0')
    obs_dim = 80 * 80
    act_dim = env.action_space.n
    logger.info('obs_dim {}, act_dim {}'.format(obs_dim, act_dim))

    # 根据parl框架构建agent
    model = Model(act_dim=act_dim)
    alg = PolicyGradient(model, lr=LEARNING_RATE)
    agent = Agent(alg, obs_dim=obs_dim, act_dim=act_dim)

    # 加载模型
    # if os.path.exists('./model.ckpt'):
    #     agent.restore('./model.ckpt')

    for i in range(1000):
        obs_list, action_list, reward_list = run_episode(env, agent)
        if i % 10 == 0:
            logger.info("Train Episode {}, Reward Sum {}.".format(
                i, sum(reward_list)))

        batch_obs = np.array(obs_list)
        batch_action = np.array(action_list)
        batch_reward = calc_reward_to_go(reward_list)

        agent.learn(batch_obs, batch_action, batch_reward)
        if (i + 1) % 100 == 0:
            total_reward = evaluate(env, agent, render=False)
            logger.info('Episode {}, Test reward: {}'.format(
                i + 1, total_reward))

    # save the parameters to ./model.ckpt
    agent.save('./model.ckpt')

老师解决方案Github

这里面还有其他几个实战项目的方案（很有用），实战项目老师解决方案大家可以进去点个star噢！