update DoubleDQN

codes/DoubleDQN/main.py

@@ -0,0 +1,126 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-12 00:48:57
+@LastEditor: John
+LastEditTime: 2020-12-22 15:39:46
+@Discription: 
+@Environment: python 3.7.7
+'''
+import gym
+import torch
+from torch.utils.tensorboard import SummaryWriter
+import os
+from agent import DQN
+from params import SEQUENCE,SAVED_MODEL_PATH,RESULT_PATH
+from params import get_args
+from utils import save_results
+
+def train(cfg):
+    print('Start to train !')
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
+    env = gym.make('CartPole-v0').unwrapped # 可google为什么unwrapped gym，此处一般不需要
+    env.seed(1) # 设置env随机种子
+    n_states = env.observation_space.shape[0]
+    n_actions = env.action_space.n
+    agent = DQN(n_states=n_states, n_actions=n_actions, device=device, gamma=cfg.gamma, epsilon_start=cfg.epsilon_start,
+                epsilon_end=cfg.epsilon_end, epsilon_decay=cfg.epsilon_decay, policy_lr=cfg.policy_lr, memory_capacity=cfg.memory_capacity, batch_size=cfg.batch_size)
+    rewards = []
+    moving_average_rewards = []
+    ep_steps = []
+    log_dir=os.path.split(os.path.abspath(__file__))[0]+"/logs/train/" + SEQUENCE
+    writer = SummaryWriter(log_dir)
+    for i_episode in range(1, cfg.train_eps+1):
+        state = env.reset() # reset环境状态
+        ep_reward = 0
+        for i_step in range(1, cfg.train_steps+1):
+            action = agent.choose_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:
+            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)
+        writer.add_scalars('rewards',{'raw':rewards[-1], 'moving_average': moving_average_rewards[-1]}, i_episode)
+        writer.add_scalar('steps_of_each_episode',
+                          ep_steps[-1], i_episode)
+    writer.close()
+    print('Complete training！')
+    ''' 保存模型 '''
+    if not os.path.exists(SAVED_MODEL_PATH): # 检测是否存在文件夹
+        os.mkdir(SAVED_MODEL_PATH)
+    agent.save_model(SAVED_MODEL_PATH+'checkpoint.pth')
+    print('model saved！')
+    '''存储reward等相关结果'''
+    save_results(rewards,moving_average_rewards,ep_steps,tag='train',result_path=RESULT_PATH)
+    
+
+def eval(cfg, saved_model_path = SAVED_MODEL_PATH):
+    print('start to eval !')
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
+    env = gym.make('CartPole-v0').unwrapped # 可google为什么unwrapped gym，此处一般不需要
+    env.seed(1) # 设置env随机种子
+    n_states = env.observation_space.shape[0]
+    n_actions = env.action_space.n
+    agent = DQN(n_states=n_states, n_actions=n_actions, device=device, gamma=cfg.gamma, epsilon_start=cfg.epsilon_start,
+                epsilon_end=cfg.epsilon_end, epsilon_decay=cfg.epsilon_decay, policy_lr=cfg.policy_lr, memory_capacity=cfg.memory_capacity, batch_size=cfg.batch_size)
+    agent.load_model(saved_model_path+'checkpoint.pth')
+    rewards = []
+    moving_average_rewards = []
+    ep_steps = []
+    log_dir=os.path.split(os.path.abspath(__file__))[0]+"/logs/eval/" + SEQUENCE
+    writer = SummaryWriter(log_dir)
+    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.choose_action(state,train=False)  # 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)
+                
+        writer.add_scalars('rewards',{'raw':rewards[-1], 'moving_average': moving_average_rewards[-1]}, i_episode)
+        writer.add_scalar('steps_of_each_episode',
+                          ep_steps[-1], i_episode)
+    writer.close()
+    '''存储reward等相关结果'''
+    save_results(rewards,moving_average_rewards,ep_steps,tag='eval',result_path=RESULT_PATH)
+    print('Complete evaling！')
+    
+if __name__ == "__main__":
+    cfg = get_args()
+    if cfg.train:
+        train(cfg)
+        eval(cfg)
+    else:
+        model_path = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"
+        eval(cfg,saved_model_path=model_path)