update

codes/DQN/task1.py

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+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-12 00:48:57
+@LastEditor: John
+LastEditTime: 2021-04-13 18:49:44
+@Discription: 
+@Environment: python 3.7.7
+'''
+import sys,os
+curr_path = os.path.dirname(__file__)
+parent_path=os.path.dirname(curr_path) 
+sys.path.append(parent_path) # add current terminal path to sys.path
+
+import gym
+import torch
+import datetime
+from DQN.agent import DQN
+from common.plot import plot_rewards
+from common.utils import save_results,make_dir,del_empty_dir
+
+SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
+SAVED_MODEL_PATH = curr_path+"/saved_model/"+SEQUENCE+'/' # path to save model
+RESULT_PATH = curr_path+"/results/"+SEQUENCE+'/' # path to save rewards
+make_dir(curr_path+"/saved_model/",curr_path+"/results/")
+del_empty_dir(curr_path+"/saved_model/",curr_path+"/results/")
+
+class DQNConfig:
+    def __init__(self):
+        self.env = 'LunarLander-v2'
+        self.algo = "DQN"  # name of algo
+        self.gamma = 0.95
+        self.epsilon_start = 1 # e-greedy策略的初始epsilon
+        self.epsilon_end = 0.01
+        self.epsilon_decay = 500
+        self.lr = 0.0001 # learning rate
+        self.memory_capacity = 1000000 # Replay Memory容量
+        self.batch_size = 64
+        self.train_eps = 300 # 训练的episode数目
+        self.train_steps = 1000
+        self.target_update = 2 # target net的更新频率
+        self.eval_eps = 20 # 测试的episode数目
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
+        self.hidden_dim = 256 # 神经网络隐藏层维度
+ 
+def train(cfg,env,agent):
+    print('Start to train !')
+    rewards = []
+    ma_rewards = [] # moveing average reward
+    for i_episode in range(cfg.train_eps):
+        state = env.reset() 
+        ep_reward = 0
+        for i_step in range(cfg.train_steps):
+            action = agent.choose_action(state) 
+            next_state, reward, done, _ = env.step(action) 
+            ep_reward += reward
+            agent.memory.push(state, action, reward, next_state, done) 
+            state = next_state 
+            agent.update()
+            if done:
+                break 
+        if i_episode % cfg.target_update == 0:
+            agent.target_net.load_state_dict(agent.policy_net.state_dict())
+        print('Episode:{}/{}, Reward:{}'.format(i_episode+1,cfg.train_eps,ep_reward))
+        rewards.append(ep_reward)
+        # 计算滑动窗口的reward
+        if ma_rewards:
+            ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
+        else:
+            ma_rewards.append(ep_reward)   
+    print('Complete training！')
+    return rewards,ma_rewards
+
+if __name__ == "__main__":
+    cfg = DQNConfig()
+    env = gym.make(cfg.env)
+    env.seed(1)
+    state_dim = env.observation_space.shape[0]
+    action_dim = env.action_space.n
+    agent = DQN(state_dim,action_dim,cfg)
+    rewards,ma_rewards = train(cfg,env,agent)
+    make_dir(SAVED_MODEL_PATH,RESULT_PATH)
+    agent.save(path=SAVED_MODEL_PATH)
+    save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)
+    plot_rewards(rewards,ma_rewards,tag="train",algo = cfg.algo,path=RESULT_PATH)
+    del_empty_dir(SAVED_MODEL_PATH,RESULT_PATH)