update DQN

codes/dqn/main.py

@@ -5,20 +5,28 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-12 00:48:57
 @LastEditor: John
-LastEditTime: 2020-08-22 18:02:56
+LastEditTime: 2020-10-15 22:00:28
 @Discription: 
 @Environment: python 3.7.7
 '''
 import gym
 import torch
-from dqn import DQN
-from plot import plot
+from agent import DQN
 import argparse
+from torch.utils.tensorboard import SummaryWriter
+import datetime
+import os
+from utils import save_results
+
+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+'/'
+RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/result/"+SEQUENCE+'/'
 
 def get_args():
     '''模型参数
     '''
     parser = argparse.ArgumentParser()
+    parser.add_argument("--train", default=1, type=int)  # 1 表示训练，0表示只进行eval
     parser.add_argument("--gamma", default=0.99,
                         type=float)  # q-learning中的gamma
     parser.add_argument("--epsilon_start", default=0.95,
@@ -31,20 +39,19 @@ def get_args():
 
     parser.add_argument("--batch_size", default=32, type=int,
                         help="batch size of memory sampling")
-    parser.add_argument("--max_episodes", default=200, type=int) # 训练的最大episode数目
-    parser.add_argument("--max_steps", default=200, type=int)
-    # 将目标网络的更新频率改为1就是普通的dqn，大于1就是double dqn
-    parser.add_argument("--target_update", default=1, type=int,
-                        help="when(every default 10 eisodes) to update target net ")
+    parser.add_argument("--train_eps", default=200, type=int) # 训练的最大episode数目
+    parser.add_argument("--train_steps", default=200, type=int)
+    parser.add_argument("--target_update", default=2, type=int,
+                        help="when(every default 2 eisodes) to update target net ") # 更新频率
+
+    parser.add_argument("--eval_eps", default=100, type=int)  # 训练的最大episode数目
+    parser.add_argument("--eval_steps", default=200,
+                        type=int)  # 训练每个episode的长度
     config = parser.parse_args()
 
     return config
-
-
-if __name__ == "__main__":
-
-    cfg = get_args()
-    # if gpu is to be used
+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.seed(1) # 设置env随机种子
@@ -55,11 +62,13 @@ if __name__ == "__main__":
     rewards = []
     moving_average_rewards = []
     ep_steps = []
-    for i_episode in range(1, cfg.max_episodes+1):
+    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.max_steps+1):
-            action = agent.select_action(state) # 根据当前环境state选择action
+        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
@@ -80,17 +89,68 @@ if __name__ == "__main__":
         else:
             moving_average_rewards.append(
                 0.9*moving_average_rewards[-1]+0.1*ep_reward)
-    # 存储reward等相关结果
-    import os
-    import numpy as np
-    output_path = os.path.dirname(__file__)+"/result/"
-    # 检测是否存在文件夹
-    if not os.path.exists(output_path):
-        os.mkdir(output_path)
-    np.save(output_path+"rewards.npy", rewards)
-    np.save(output_path+"moving_average_rewards.npy", moving_average_rewards)
-    np.save(output_path+"steps.npy", ep_steps)
-    print('Complete！')
-    plot(rewards)
-    plot(moving_average_rewards, ylabel="moving_average_rewards")
-    plot(ep_steps, ylabel="steps_of_each_episode")
+        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 ! \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.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)