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+# 使用Policy-Based方法实现Pendulum-v0
+
+使用Policy-Based方法比如DDPG等实现Pendulum-v0环境
+
+## Pendulum-v0
+
+![image-20200820174814084](img/image-20200820174814084.png)
+
+钟摆以随机位置开始，目标是将其摆动，使其保持向上直立。动作空间是连续的，值的区间为[-2,2]。每个step给的reward最低为-16.27，最高为0。
+
+环境建立如下：
+
+```python
+env = gym.make('Pendulum-v0') 
+env.seed(1) # 设置env随机种子
+n_states = env.observation_space.shape[0] # 获取总的状态数
+```
+
+## 强化学习基本接口
+
+```python
+rewards = [] # 记录总的rewards
+moving_average_rewards = [] # 记录总的经滑动平均处理后的rewards
+ep_steps = []
+for i_episode in range(1, cfg.max_episodes+1): # cfg.max_episodes为最大训练的episode数
+    state = env.reset() # reset环境状态
+    ep_reward = 0
+    for i_step in range(1, cfg.max_steps+1): # cfg.max_steps为每个episode的补偿
+        action = agent.select_action(state) # 根据当前环境state选择action
+        next_state, reward, done, _ = env.step(action) # 更新环境参数
+        ep_reward += reward
+        agent.memory.push(state, action, reward, next_state, done) # 将state等这些transition存入memory
+        state = next_state # 跳转到下一个状态
+        agent.update() # 每步更新网络
+        if done:
+            break
+    # 更新target network，复制DQN中的所有weights and biases
+    if i_episode % cfg.target_update == 0: #  cfg.target_update为target_net的更新频率
+        agent.target_net.load_state_dict(agent.policy_net.state_dict())
+    print('Episode:', i_episode, ' Reward: %i' %
+          int(ep_reward), 'n_steps:', i_step, 'done: ', done,' Explore: %.2f' % agent.epsilon)
+    ep_steps.append(i_step)
+    rewards.append(ep_reward)
+    # 计算滑动窗口的reward
+    if i_episode == 1:
+        moving_average_rewards.append(ep_reward)
+    else:
+        moving_average_rewards.append(
+            0.9*moving_average_rewards[-1]+0.1*ep_reward)
+```
+
+## 任务要求
+
+训练并绘制reward以及滑动平均后的reward随epiosde的变化曲线图并记录超参数写成报告，图示如下：
+
+![moving_average_rewards](img/moving_average_rewards-8929361.png)
+
+### 注意
+
+1. 本次环境action范围在[-2,2]之间，而神经网络中输出的激活函数tanh在[0,1]，可以使用NormalizedActions(gym.ActionWrapper)的方法解决
+2. 由于本次环境为惯性系统，建议增加Ornstein-Uhlenbeck噪声提高探索率，可参考[知乎文章](https://zhuanlan.zhihu.com/p/96720878)
+3. 推荐多次试验保存rewards，然后使用searborn绘制，可参考[CSDN](https://blog.csdn.net/JohnJim0/article/details/106715402)
+
+### 代码清单
+
+**main.py**：保存强化学习基本接口，以及相应的超参数，可使用argparse
+
+**model.py**：保存神经网络，比如全链接网络
+
+**ddpg.py**: 保存算法模型，主要包含select_action和update两个函数
+
+**memory.py**：保存Replay Buffer
+
+**plot.py**：保存相关绘制函数
+
+**noise.py**：保存噪声相关
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