update DQN

codes/dqn/main.py

@@ -5,7 +5,7 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-12 00:48:57
 @LastEditor: John
-LastEditTime: 2020-10-15 22:00:28
+LastEditTime: 2020-11-23 11:58:17
 @Discription: 
 @Environment: python 3.7.7
 '''
@@ -16,7 +16,7 @@ import argparse
 from torch.utils.tensorboard import SummaryWriter
 import datetime
 import os
-from utils import save_results
+from utils import save_results,save_model
 
 SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
 SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/'
@@ -53,7 +53,7 @@ def get_args():
 def train(cfg):
     print('Start to train ! \n')
     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 = gym.make('CartPole-v0')
     env.seed(1) # 设置env随机种子
     n_states = env.observation_space.shape[0]
     n_actions = env.action_space.n
@@ -95,10 +95,7 @@ def train(cfg):
     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！')
+    save_model(agent,model_path=SAVED_MODEL_PATH)
     '''存储reward等相关结果'''
     save_results(rewards,moving_average_rewards,ep_steps,tag='train',result_path=RESULT_PATH)
     
@@ -110,7 +107,7 @@ def eval(cfg, saved_model_path = SAVED_MODEL_PATH):
     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,
+    agent = DQN(n_states=n_states, n_actions=n_actions, device="cpu", 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 = []