强化学习从基础到进阶-案例与实践[4.2]：深度Q网络DQN-Cart pole游戏展示

• 强化学习（Reinforcement learning，简称RL）是机器学习中的一个领域，区别与监督学习和无监督学习，强调如何基于环境而行动，以取得最大化的预期利益。
• 基本操作步骤：智能体agent在环境environment中学习，根据环境的状态state（或观测到的observation），执行动作action，并根据环境的反馈reward（奖励）来指导更好的动作。

比如本项目的Cart pole小游戏中，agent就是动图中的杆子，杆子有向左向右两种action。

## 安装依赖
!pip install pygame
!pip install gym
!pip install atari_py
!pip install parl

import gym
import os
import random
import collections

import paddle
import paddle.nn as nn
import numpy as np
import paddle.nn.functional as F

1.经验回放部分

经验回放主要做的事情是：把结果存入经验池，然后经验池中随机取出一条结果进行训练。

这样做有两个好处：

1. 减少样本之间的关联性
2. 提高样本的利用率

之所以加入experience replay是因为样本是从游戏中的连续帧获得的，这与简单的reinforcement learning问题相比，样本的关联性大了很多，如果没有experience replay，算法在连续一段时间内基本朝着同一个方向做gradient descent，那么同样的步长下这样直接计算gradient就有可能不收敛。因此experience replay是从一个memory pool中随机选取了一些expeirence，然后再求梯度，从而避免了这个问题。

class ReplayMemory(object):
    def __init__(self, max_size):
        self.buffer = collections.deque(maxlen=max_size)

    # 增加一条经验到经验池中
    def append(self, exp):
        self.buffer.append(exp)

    # 从经验池中选取N条经验出来
    def sample(self, batch_size):
        mini_batch = random.sample(self.buffer, batch_size)
        obs_batch, action_batch, reward_batch, next_obs_batch, done_batch = [], [], [], [], []

        for experience in mini_batch:
            s, a, r, s_p, done = experience
            obs_batch.append(s)
            action_batch.append(a)
            reward_batch.append(r)
            next_obs_batch.append(s_p)
            done_batch.append(done)

        return np.array(obs_batch).astype('float32'), np.array(action_batch).astype('float32'), np.array(reward_batch).astype('float32'), np.array(next_obs_batch).astype('float32'), np.array(done_batch).astype('float32')

    def __len__(self):
        return len(self.buffer)

2.DQN

DQN算法较普通算法在经验回放和固定Q目标有了较大的改进，主要原因：

• 经验回放：他充分利用了off-colicp的优势，通过训练把结果（成绩）存入Q表格，然后随机从表格中取出一条结果进行优化。这样子一方面可以：减少样本之间的关联性另一方面：提高样本的利用率 注：训练结果会存进Q表格，当Q表格满了以后，存进来的数据会把最早存进去的数据“挤出去”（弹出）
• 固定Q目标他解决了算法更新不平稳的问题。 和监督学习做比较，监督学习的最终值要逼近实际结果，这个结果是固定的，但是我们的DQN却不是，他的目标值是经过神经网络以后的一个值，那么这个值是变动的不好拟合，怎么办，DQN团队想到了一个很好的办法，让这个值在一定时间里面保持不变，这样子这个目标就可以确定了，然后目标值更新以后更加接近实际结果，可以更好的进行训练。

3.模型Model

这里的模型可以根据自己的需求选择不同的神经网络组建。

DQN用来定义前向(Forward)网络，可以自由的定制自己的网络结构。

class DQN(nn.Layer):
    def __init__(self, outputs):
        super(DQN, self).__init__()
        self.linear1 = nn.Linear(in_features=4, out_features=128)
        self.linear2 = nn.Linear(in_features=128, out_features=24)
        self.linear3 = nn.Linear(in_features=24, out_features=outputs)

    def forward(self, x):
        x = self.linear1(x)
        x = F.relu(x)
        x = self.linear2(x)
        x = F.relu(x)
        x = self.linear3(x)
        return x

4.智能体Agent的学习函数

这里包括模型探索与模型训练两个部分

Agent负责算法与环境的交互，在交互过程中把生成的数据提供给Algorithm来更新模型(Model)，数据的预处理流程也一般定义在这里。

def sample(obs, MODEL):
    global E_GREED
    global ACTION_DIM
    global E_GREED_DECREMENT
    sample = np.random.rand()  # 产生0~1之间的小数
    if sample < E_GREED:
        act = np.random.randint(ACTION_DIM)  # 探索：每个动作都有概率被选择
    else:
        obs = np.expand_dims(obs, axis=0)
        obs = paddle.to_tensor(obs, dtype='float32')
        act = MODEL(obs)
        act = np.argmax(act.numpy())  # 选择最优动作
    E_GREED = max(0.01, E_GREED - E_GREED_DECREMENT)  # 随着训练逐步收敛，探索的程度慢慢降低
    return act


def learn(obs, act, reward, next_obs, terminal, TARGET_MODEL, MODEL):
    global global_step
    # 每隔200个training steps同步一次model和target_model的参数
    if global_step % 50 == 0:
        TARGET_MODEL.load_dict(MODEL.state_dict())
    global_step += 1

    obs = np.array(obs).astype('float32')
    next_obs = np.array(next_obs).astype('float32')
    # act = np.expand_dims(act, -1)
    cost = optimize_model(obs, act, reward, next_obs,
                          terminal, TARGET_MODEL, MODEL)  # 训练一次网络
    return cost


def optimize_model(obs, action, reward, next_obs, terminal, TARGET_MODEL, MODEL):
    """
    使用DQN算法更新self.model的value网络
    """
    # 从target_model中获取 max Q' 的值，用于计算target_Q
    global E_GREED
    global ACTION_DIM
    global E_GREED_DECREMENT
    global GAMMA
    global LEARNING_RATE
    global opt

    opt = paddle.optimizer.Adam(learning_rate=LEARNING_RATE,
                                parameters=MODEL.parameters())  # 优化器(动态图)

    obs = paddle.to_tensor(obs)
    next_obs = paddle.to_tensor(next_obs)

    next_pred_value = TARGET_MODEL(next_obs).detach()
    best_v = paddle.max(next_pred_value, axis=1)
    target = reward + (1.0 - terminal) * GAMMA * best_v.numpy()
    target = paddle.to_tensor(target)
    pred_value = MODEL(obs)  # 获取Q预测值
    # 将action转onehot向量，比如：3 => [0,0,0,1,0]
    action = paddle.to_tensor(action.astype('int32'))
    action_onehot = F.one_hot(action, ACTION_DIM)
    action_onehot = paddle.cast(action_onehot, dtype='float32')
    # 下面一行是逐元素相乘，拿到action对应的 Q(s,a)
    pred_action_value = paddle.sum(paddle.multiply(action_onehot, pred_value), axis=1)
    # 计算 Q(s,a) 与 target_Q的均方差，得到loss
    cost = F.square_error_cost(pred_action_value, target)
    cost = paddle.mean(cost)
    avg_cost = cost
    cost.backward()
    opt.step()
    opt.clear_grad()

    return avg_cost.numpy()

5.模型梯度更新算法

def run_train(env, rpm, TARGET_MODEL, MODEL):
    MODEL.train()
    TARGET_MODEL.train()
    total_reward = 0
    obs = env.reset()

    global global_step
    while True:
        global_step += 1
        # 获取随机动作和执行游戏
        action = sample(obs, MODEL)

        next_obs, reward, isOver, info = env.step(action)

        # 记录数据
        rpm.append((obs, action, reward, next_obs, isOver))

        # 在预热完成之后，每隔LEARN_FREQ步数就训练一次
        if (len(rpm) > MEMORY_WARMUP_SIZE) and (global_step % LEARN_FREQ == 0):
            (batch_obs, batch_action, batch_reward, batch_next_obs, batch_isOver) = rpm.sample(BATCH_SIZE)
            train_loss = learn(batch_obs, batch_action, batch_reward,
                               batch_next_obs, batch_isOver, TARGET_MODEL, MODEL)

        total_reward += reward
        obs = next_obs.astype('float32')

        # 结束游戏
        if isOver:
            break
    return total_reward


def evaluate(model, env, render=False):
    model.eval()
    eval_reward = []
    for i in range(5):
        obs = env.reset()
        episode_reward = 0
        while True:
            obs = np.expand_dims(obs, axis=0)
            obs = paddle.to_tensor(obs, dtype='float32')
            action = model(obs)
            action = np.argmax(action.numpy())
            obs, reward, done, _ = env.step(action)
            episode_reward += reward
            if render:
                env.render()
            if done:
                break
        eval_reward.append(episode_reward)
    return np.mean(eval_reward)

6.训练函数与验证函数

设置超参数

LEARN_FREQ = 5  # 训练频率，不需要每一个step都learn，攒一些新增经验后再learn，提高效率
MEMORY_SIZE = 20000  # replay memory的大小，越大越占用内存
MEMORY_WARMUP_SIZE = 200  # replay_memory 里需要预存一些经验数据，再开启训练
BATCH_SIZE = 32  # 每次给agent learn的数据数量，从replay memory随机里sample一批数据出来
LEARNING_RATE = 0.001  # 学习率大小
GAMMA = 0.99  # reward 的衰减因子，一般取 0.9 到 0.999 不等

E_GREED = 0.1  # 探索初始概率
E_GREED_DECREMENT = 1e-6  # 在训练过程中，降低探索的概率
MAX_EPISODE = 20000  # 训练次数
SAVE_MODEL_PATH = "models/save"  # 保存模型路径
OBS_DIM = None
ACTION_DIM = None
global_step = 0

def main():
    global OBS_DIM
    global ACTION_DIM

    train_step_list = []
    train_reward_list = []
    evaluate_step_list = []
    evaluate_reward_list = []

    # 初始化游戏
    env = gym.make('CartPole-v0')
    # 图像输入形状和动作维度
    action_dim = env.action_space.n
    obs_dim = env.observation_space.shape
    OBS_DIM = obs_dim
    ACTION_DIM = action_dim
    max_score = -int(1e4)

    # 创建存储执行游戏的内存
    rpm = ReplayMemory(MEMORY_SIZE)
    MODEL = DQN(ACTION_DIM)
    TARGET_MODEL = DQN(ACTION_DIM)
    # if os.path.exists(os.path.dirname(SAVE_MODEL_PATH)):
    #     MODEL_DICT = paddle.load(SAVE_MODEL_PATH+'.pdparams')
    #     MODEL.load_dict(MODEL_DICT)  # 加载模型参数
    print("filling memory...")
    while len(rpm) < MEMORY_WARMUP_SIZE:
        run_train(env, rpm, TARGET_MODEL, MODEL)
    print("filling memory done")

    # 开始训练
    episode = 0

    print("start training...")
    # 训练max_episode个回合，test部分不计算入episode数量
    while episode < MAX_EPISODE:
        # train part
        for i in range(0, int(50)):
            # First we need a state
            total_reward = run_train(env, rpm, TARGET_MODEL, MODEL)
            episode += 1

        # print("episode:{}    reward:{}".format(episode, str(total_reward)))

        # test part
        # print("start evaluation...")
        eval_reward = evaluate(TARGET_MODEL, env)
        print('episode:{}    e_greed:{}   test_reward:{}'.format(episode, E_GREED, eval_reward))

        evaluate_step_list.append(episode)
        evaluate_reward_list.append(eval_reward)

        # if eval_reward > max_score or not os.path.exists(os.path.dirname(SAVE_MODEL_PATH)):
        #     max_score = eval_reward
        #     paddle.save(TARGET_MODEL.state_dict(), SAVE_MODEL_PATH+'.pdparams')  # 保存模型


if __name__ == '__main__':
    main()

filling memory...
filling memory done
start training...
episode:50 e_greed:0.0992949999999993 test_reward:9.0
episode:100 e_greed:0.0987909999999988 test_reward:9.8
episode:150 e_greed:0.09827199999999828 test_reward:10.0
episode:200 e_greed:0.09777599999999778 test_reward:8.8
episode:250 e_greed:0.09726999999999728 test_reward:9.0
episode:300 e_greed:0.09676199999999677 test_reward:10.0
episode:350 e_greed:0.0961919999999962 test_reward:14.8

项目链接fork一下即可运行

https://www.heywhale.com/mw/project/649e7d3f70567260f8f11d2b

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