add some codes

codes/ddpg/main.py

@@ -0,0 +1,89 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-11 20:58:21
+@LastEditor: John
+@LastEditTime: 2020-07-20 23:01:02
+@Discription: 
+@Environment: python 3.7.7
+'''
+import torch
+import gym
+
+from ddpg import DDPG
+from env import NormalizedActions
+from noise import OUNoise
+from plot import plot
+
+import argparse
+
+def get_args():
+    '''模型建立好之后只需要在这里调参
+    '''
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--gamma", default=0.99, type=float) # q-learning中的gamma
+    parser.add_argument("--critic_lr", default=1e-3, type=float) # critic学习率
+    parser.add_argument("--actor_lr", default=1e-4, type=float)
+
+    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("--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__":
+
+    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] 
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    agent=DDPG(n_states,n_actions,device="cpu", critic_lr=1e-3,
+                 actor_lr=1e-4, gamma=0.99, soft_tau=1e-2, memory_capacity=100000, batch_size=128)
+
+    rewards  = []
+    moving_average_rewards = []
+    for i_episode in range(1,cfg.max_episodes+1):
+        state=env.reset()
+        ou_noise.reset()
+        ep_reward = 0
+        for i_step in range(1,cfg.max_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)
+            ep_reward += reward
+            agent.memory.push(state, action, reward, next_state, done)
+            agent.update()
+            state = next_state
+            if done:
+                break
+        print('Episode:', i_episode, ' Reward: %i' % int(ep_reward),)
+        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！')
+    import os
+    import numpy as np
+    output_path = os.path.dirname(__file__)+"/result/"
+    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)
+  
+    plot(rewards)
+    plot(moving_average_rewards,ylabel="moving_average_rewards")