update

codes/DQN/main.py

@@ -5,12 +5,17 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-12 00:48:57
 @LastEditor: John
-LastEditTime: 2021-03-26 17:17:17
+LastEditTime: 2021-03-30 16:59:19
 @Discription: 
 @Environment: python 3.7.7
 '''
 import sys,os
-sys.path.append(os.getcwd()) # 添加当前终端路径
+from pathlib import Path
+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
@@ -18,58 +23,52 @@ from DQN.agent import DQN
 from common.plot import plot_rewards
 from common.utils import save_results
 
-SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
-SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/' # 生成保存的模型路径
-if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"): # 检测是否存在文件夹
-    os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/")
-if not os.path.exists(SAVED_MODEL_PATH): # 检测是否存在文件夹
+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
+if not os.path.exists(curr_path+"/saved_model/"): 
+    os.mkdir(curr_path+"/saved_model/")
+if not os.path.exists(SAVED_MODEL_PATH): 
     os.mkdir(SAVED_MODEL_PATH)
-RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/results/"+SEQUENCE+'/' # 存储reward的路径
-if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/results/"): # 检测是否存在文件夹
-    os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/results/")
-if not os.path.exists(RESULT_PATH): # 检测是否存在文件夹
+RESULT_PATH = curr_path+"/results/"+SEQUENCE+'/' # path to save rewards
+if not os.path.exists(curr_path+"/results/"): 
+    os.mkdir(curr_path+"/results/")
+if not os.path.exists(RESULT_PATH): 
     os.mkdir(RESULT_PATH)
 
 class DQNConfig:
     def __init__(self):
-        self.algo = "DQN" # 算法名称
-        self.gamma = 0.99
-        self.epsilon_start = 0.95 # e-greedy策略的初始epsilon
+        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 = 200
-        self.lr = 0.01 # 学习率
-        self.memory_capacity = 800 # Replay Memory容量
-        self.batch_size = 64
+        self.epsilon_decay = 500
+        self.lr = 0.0001 # learning rate
+        self.memory_capacity = 10000 # Replay Memory容量
+        self.batch_size = 32
         self.train_eps = 300 # 训练的episode数目
-        self.train_steps = 200 # 训练每个episode的最大长度
         self.target_update = 2 # target net的更新频率
         self.eval_eps = 20 # 测试的episode数目
-        self.eval_steps = 200 # 测试每个episode的最大长度
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
-        self.hidden_dim = 128 # 神经网络隐藏层维度
+        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 = [] # 滑动平均的reward
-    ep_steps = []
+    ma_rewards = [] # moveing average reward
     for i_episode in range(cfg.train_eps):
-        state = env.reset() # reset环境状态
+        state = env.reset() 
+        done = False
         ep_reward = 0
-        for i_step in range(cfg.train_steps):
-            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
+        while not done:
+            action = agent.choose_action(state) 
+            next_state, reward, done, _ = env.step(action) 
             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
+            agent.memory.push(state, action, reward, next_state, done) 
+            state = next_state 
+            agent.update() 
         if i_episode % cfg.target_update == 0:
             agent.target_net.load_state_dict(agent.policy_net.state_dict())
-        print('Episode:{}/{}, Reward:{}, Steps:{}, Done:{}'.format(i_episode+1,cfg.train_eps,ep_reward,i_step+1,done))
-        ep_steps.append(i_step)
+        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:
@@ -82,8 +81,8 @@ def train(cfg,env,agent):
 
 if __name__ == "__main__":
     cfg = DQNConfig()
-    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
+    env = gym.make('CartPole-v0')
+    env.seed(1)
     state_dim = env.observation_space.shape[0]
     action_dim = env.action_space.n
     agent = DQN(state_dim,action_dim,cfg)