update

codes/QLearning/task0_train.py

@@ -5,7 +5,7 @@ Author: John
 Email: johnjim0816@gmail.com
 Date: 2020-09-11 23:03:00
 LastEditor: John
-LastEditTime: 2021-04-29 17:01:08
+LastEditTime: 2021-05-06 17:04:38
 Discription: 
 Environment: 
 '''
@@ -15,6 +15,7 @@ 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 envs.gridworld_env import CliffWalkingWapper
@@ -37,6 +38,8 @@ class QlearningConfig:
         self.epsilon_end = 0.01 # e-greedy策略中的终止epsilon
         self.epsilon_decay = 200 # e-greedy策略中epsilon的衰减率
         self.lr = 0.1 # learning rate
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # check gpu
+
         
 def env_agent_config(cfg,seed=1):
     env = gym.make(cfg.env)  
@@ -48,6 +51,8 @@ def env_agent_config(cfg,seed=1):
     return env,agent
 
 def train(cfg,env,agent):
+    print('Start to train !')
+    print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
     rewards = []  
     ma_rewards = [] # moving average reward
     for i_ep in range(cfg.train_eps):
@@ -67,11 +72,14 @@ def train(cfg,env,agent):
         else:
             ma_rewards.append(ep_reward)
         print("Episode:{}/{}: reward:{:.1f}".format(i_ep+1, cfg.train_eps,ep_reward))
+    print('Complete training！')
     return rewards,ma_rewards
     
 def eval(cfg,env,agent):
     # env = gym.make("FrozenLake-v0", is_slippery=False)  # 0 left, 1 down, 2 right, 3 up
     # env = FrozenLakeWapper(env)
+    print('Start to eval !')
+    print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
     rewards = []  # 记录所有episode的reward
     ma_rewards = [] # 滑动平均的reward
     for i_ep in range(cfg.eval_eps):
@@ -90,6 +98,7 @@ def eval(cfg,env,agent):
         else:
             ma_rewards.append(ep_reward)
         print(f"Episode:{i_ep+1}/{cfg.eval_eps}, reward:{ep_reward:.1f}")
+    print('Complete evaling！')
     return rewards,ma_rewards
     
 if __name__ == "__main__":