add ddqn

codes/ddpg/main.py

@@ -5,7 +5,7 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-11 20:58:21
 @LastEditor: John
-@LastEditTime: 2020-07-20 23:01:02
+LastEditTime: 2020-09-02 01:24:50
 @Discription: 
 @Environment: python 3.7.7
 '''
@@ -31,15 +31,15 @@ def get_args():
     parser.add_argument("--memory_capacity", default=10000, type=int,help="capacity of Replay Memory")
 
     parser.add_argument("--batch_size", default=128, type=int,help="batch size of memory sampling")
-    parser.add_argument("--max_episodes", default=200, type=int)
-    parser.add_argument("--max_steps", default=200, type=int)
+    parser.add_argument("--train_eps", default=200, type=int)
+    parser.add_argument("--train_steps", default=200, type=int)
+    parser.add_argument("--eval_eps", default=200, type=int) # 训练的最大episode数目
+    parser.add_argument("--eval_steps", default=200, type=int) # 训练每个episode的长度
     parser.add_argument("--target_update", default=4, type=int,help="when(every default 10 eisodes) to update target net ")
     config = parser.parse_args()
-
     return config
 
-if __name__ == "__main__":
-
+def train():
     cfg = get_args()
     env = NormalizedActions(gym.make("Pendulum-v0"))
     
@@ -54,11 +54,12 @@ if __name__ == "__main__":
 
     rewards  = []
     moving_average_rewards = []
-    for i_episode in range(1,cfg.max_episodes+1):
+    ep_steps = []
+    for i_episode in range(1,cfg.train_eps+1):
         state=env.reset()
         ou_noise.reset()
         ep_reward = 0
-        for i_step in range(1,cfg.max_steps+1):
+        for i_step in range(1,cfg.train_steps+1):
             action = agent.select_action(state)   
             action = ou_noise.get_action(action, i_step) # 即paper中的random process
             next_state, reward, done, _ = env.step(action)
@@ -68,22 +69,79 @@ if __name__ == "__main__":
             state = next_state
             if done:
                 break
-        print('Episode:', i_episode, ' Reward: %i' % int(ep_reward),)
+        print('Episode:', i_episode, ' Reward: %i' % int(ep_reward),'n_steps:', i_step)
+        ep_steps.append(i_step)
         rewards.append(ep_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)
     print('Complete！')
+    # 保存模型
     import os
     import numpy as np
+    save_path = os.path.dirname(__file__)+"/saved_model/"
+    if not os.path.exists(save_path):
+        os.mkdir(save_path)
+    agent.save_model(save_path+'checkpoint.pth')
+    # 存储reward等相关结果
     output_path = os.path.dirname(__file__)+"/result/"
+    # 检测是否存在文件夹
     if not os.path.exists(output_path):
         os.mkdir(output_path)
     np.save(output_path+"rewards.npy", rewards)
     np.save(output_path+"moving_average_rewards.npy", moving_average_rewards)
-  
+    np.save(output_path+"steps.npy", ep_steps)
     plot(rewards)
-    plot(moving_average_rewards,ylabel="moving_average_rewards")
+    plot(moving_average_rewards,ylabel="moving_average_rewards")
+    plot(ep_steps, ylabel="steps_of_each_episode")
+    
+def eval():
+    cfg = get_args()
+    env = NormalizedActions(gym.make("Pendulum-v0"))
+    
+    # 增加action噪声
+    ou_noise = OUNoise(env.action_space)
+    
+    n_states  = env.observation_space.shape[0] 
+    n_actions = env.action_space.shape[0] 
+    agent=DDPG(n_states,n_actions, critic_lr=1e-3,
+                 actor_lr=1e-4, gamma=0.99, soft_tau=1e-2, memory_capacity=100000, batch_size=128)
+
+    import os
+    save_path = os.path.dirname(__file__)+"/saved_model/"
+    if not os.path.exists(save_path):
+        os.mkdir(save_path)
+    agent.load_model(save_path+'checkpoint.pth')
+    rewards = []
+    moving_average_rewards = []
+    ep_steps = []
+    for i_episode in range(1, cfg.eval_eps+1):
+        state = env.reset() # reset环境状态
+        ep_reward = 0
+        for i_step in range(1, cfg.eval_steps+1):
+            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
+            state = next_state # 跳转到下一个状态
+            if done:
+                break
+        print('Episode:', i_episode, ' Reward: %i' %
+              int(ep_reward), 'n_steps:', i_step, 'done: ', done)
+        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)
+    plot(rewards,save_fig=False)
+    plot(moving_average_rewards, ylabel="moving_average_rewards",save_fig=False)
+    plot(ep_steps, ylabel="steps_of_each_episode",save_fig=False)
+    
+
+if __name__ == "__main__":
+    # train()
+    eval()