diff --git a/codes/A2C/a2c.py b/codes/A2C/a2c.py
index bd26785..ba0ed7c 100644
--- a/codes/A2C/a2c.py
+++ b/codes/A2C/a2c.py
@@ -5,10 +5,11 @@ Author: JiangJi
 Email: johnjim0816@gmail.com
 Date: 2021-05-03 22:16:08
 LastEditor: JiangJi
-LastEditTime: 2021-05-03 22:23:48
+LastEditTime: 2022-07-20 23:54:40
 Discription: 
 Environment: 
 '''
+import torch
 import torch.optim as optim
 import torch.nn as nn
 import torch.nn.functional as F
@@ -42,7 +43,7 @@ class A2C:
     '''
     def __init__(self,n_states,n_actions,cfg) -> None:
         self.gamma = cfg.gamma
-        self.device = cfg.device
+        self.device = torch.device(cfg.device)
         self.model = ActorCritic(n_states, n_actions, cfg.hidden_size).to(self.device)
         self.optimizer = optim.Adam(self.model.parameters())
 
diff --git a/codes/A2C/outputs/CartPole-v0/20220713-221850/results/params.json b/codes/A2C/outputs/CartPole-v0/20220713-221850/results/params.json
new file mode 100644
index 0000000..2773964
--- /dev/null
+++ b/codes/A2C/outputs/CartPole-v0/20220713-221850/results/params.json
@@ -0,0 +1,14 @@
+{
+    "algo_name": "A2C",
+    "env_name": "CartPole-v0",
+    "n_envs": 8,
+    "max_steps": 20000,
+    "n_steps": 5,
+    "gamma": 0.99,
+    "lr": 0.001,
+    "hidden_dim": 256,
+    "deivce": "cpu",
+    "result_path": "C:\\Users\\24438\\Desktop\\rl-tutorials/outputs/CartPole-v0/20220713-221850/results/",
+    "model_path": "C:\\Users\\24438\\Desktop\\rl-tutorials/outputs/CartPole-v0/20220713-221850/models/",
+    "save_fig": true
+}
\ No newline at end of file
diff --git a/codes/A2C/outputs/CartPole-v0/20220713-221850/results/params.txt b/codes/A2C/outputs/CartPole-v0/20220713-221850/results/params.txt
deleted file mode 100644
index 2daca8c..0000000
--- a/codes/A2C/outputs/CartPole-v0/20220713-221850/results/params.txt
+++ /dev/null
@@ -1,14 +0,0 @@
------------------- start ------------------
-algo_name : A2C
-env_name : CartPole-v0
-n_envs : 8
-max_steps : 30000
-n_steps : 5
-gamma : 0.99
-lr : 0.001
-hidden_dim : 256
-result_path : c:\Users\24438\Desktop\rl-tutorials\codes\A2C/outputs/CartPole-v0/20220713-221850/results/
-model_path : c:\Users\24438\Desktop\rl-tutorials\codes\A2C/outputs/CartPole-v0/20220713-221850/models/
-save_fig : True
-device : cuda
-------------------- end -------------------
\ No newline at end of file
diff --git a/codes/A2C/task0.ipynb b/codes/A2C/task0.ipynb
deleted file mode 100644
index aa9b772..0000000
--- a/codes/A2C/task0.ipynb
+++ /dev/null
@@ -1,265 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "from pathlib import Path\n",
-    "curr_path = str(Path().absolute()) # 当前路径\n",
-    "parent_path = str(Path().absolute().parent) # 父路径\n",
-    "sys.path.append(parent_path) # 添加路径到系统路径\n",
-    "import math\n",
-    "import random\n",
-    "\n",
-    "import gym\n",
-    "import numpy as np\n",
-    "\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "import torch.nn.functional as F\n",
-    "from torch.distributions import Categorical\n",
-    "\n",
-    "from IPython.display import clear_output\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "use_cuda = torch.cuda.is_available()\n",
-    "device   = torch.device(\"cuda\" if use_cuda else \"cpu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from common.multiprocessing_env import SubprocVecEnv\n",
-    "\n",
-    "num_envs = 16\n",
-    "env_name = \"CartPole-v0\"\n",
-    "\n",
-    "def make_env():\n",
-    "    def _thunk():\n",
-    "        env = gym.make(env_name)\n",
-    "        return env\n",
-    "\n",
-    "    return _thunk\n",
-    "\n",
-    "envs = [make_env() for i in range(num_envs)]\n",
-    "envs = SubprocVecEnv(envs)\n",
-    "\n",
-    "env = gym.make(env_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class ActorCritic(nn.Module):\n",
-    "    def __init__(self, num_inputs, num_outputs, hidden_size, std=0.0):\n",
-    "        super(ActorCritic, self).__init__()\n",
-    "        \n",
-    "        self.critic = nn.Sequential(\n",
-    "            nn.Linear(num_inputs, hidden_size),\n",
-    "            nn.ReLU(),\n",
-    "            nn.Linear(hidden_size, 1)\n",
-    "        )\n",
-    "        \n",
-    "        self.actor = nn.Sequential(\n",
-    "            nn.Linear(num_inputs, hidden_size),\n",
-    "            nn.ReLU(),\n",
-    "            nn.Linear(hidden_size, num_outputs),\n",
-    "            nn.Softmax(dim=1),\n",
-    "        )\n",
-    "        \n",
-    "    def forward(self, x):\n",
-    "        value = self.critic(x)\n",
-    "        probs = self.actor(x)\n",
-    "        dist  = Categorical(probs)\n",
-    "        return dist, value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot(frame_idx, rewards):\n",
-    "    clear_output(True)\n",
-    "    plt.figure(figsize=(20,5))\n",
-    "    plt.subplot(131)\n",
-    "    plt.title('frame %s. reward: %s' % (frame_idx, rewards[-1]))\n",
-    "    plt.plot(rewards)\n",
-    "    plt.show()\n",
-    "    \n",
-    "def test_env(vis=False):\n",
-    "    state = env.reset()\n",
-    "    if vis: env.render()\n",
-    "    done = False\n",
-    "    total_reward = 0\n",
-    "    while not done:\n",
-    "        state = torch.FloatTensor(state).unsqueeze(0).to(device)\n",
-    "        dist, _ = model(state)\n",
-    "        next_state, reward, done, _ = env.step(dist.sample().cpu().numpy()[0])\n",
-    "        state = next_state\n",
-    "        if vis: env.render()\n",
-    "        total_reward += reward\n",
-    "    return total_reward"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def compute_returns(next_value, rewards, masks, gamma=0.99):\n",
-    "    R = next_value\n",
-    "    returns = []\n",
-    "    for step in reversed(range(len(rewards))):\n",
-    "        R = rewards[step] + gamma * R * masks[step]\n",
-    "        returns.insert(0, R)\n",
-    "    return returns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_inputs  = envs.observation_space.shape[0]\n",
-    "num_outputs = envs.action_space.n\n",
-    "\n",
-    "#Hyper params:\n",
-    "hidden_size = 256\n",
-    "lr          = 3e-4\n",
-    "num_steps   = 5\n",
-    "\n",
-    "model = ActorCritic(num_inputs, num_outputs, hidden_size).to(device)\n",
-    "optimizer = optim.Adam(model.parameters())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_frames   = 20000\n",
-    "frame_idx    = 0\n",
-    "test_rewards = []"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "state = envs.reset()\n",
-    "\n",
-    "while frame_idx < max_frames:\n",
-    "\n",
-    "    log_probs = []\n",
-    "    values    = []\n",
-    "    rewards   = []\n",
-    "    masks     = []\n",
-    "    entropy = 0\n",
-    "\n",
-    "    for _ in range(num_steps):\n",
-    "        state = torch.FloatTensor(state).to(device)\n",
-    "        dist, value = model(state)\n",
-    "\n",
-    "        action = dist.sample()\n",
-    "        next_state, reward, done, _ = envs.step(action.cpu().numpy())\n",
-    "\n",
-    "        log_prob = dist.log_prob(action)\n",
-    "        entropy += dist.entropy().mean()\n",
-    "        \n",
-    "        log_probs.append(log_prob)\n",
-    "        values.append(value)\n",
-    "        rewards.append(torch.FloatTensor(reward).unsqueeze(1).to(device))\n",
-    "        masks.append(torch.FloatTensor(1 - done).unsqueeze(1).to(device))\n",
-    "        \n",
-    "        state = next_state\n",
-    "        frame_idx += 1\n",
-    "        \n",
-    "        if frame_idx % 1000 == 0:\n",
-    "            test_rewards.append(np.mean([test_env() for _ in range(10)]))\n",
-    "            plot(frame_idx, test_rewards)\n",
-    "            \n",
-    "    next_state = torch.FloatTensor(next_state).to(device)\n",
-    "    _, next_value = model(next_state)\n",
-    "    returns = compute_returns(next_value, rewards, masks)\n",
-    "    \n",
-    "    log_probs = torch.cat(log_probs)\n",
-    "    returns   = torch.cat(returns).detach()\n",
-    "    values    = torch.cat(values)\n",
-    "\n",
-    "    advantage = returns - values\n",
-    "\n",
-    "    actor_loss  = -(log_probs * advantage.detach()).mean()\n",
-    "    critic_loss = advantage.pow(2).mean()\n",
-    "\n",
-    "    loss = actor_loss + 0.5 * critic_loss - 0.001 * entropy\n",
-    "\n",
-    "    optimizer.zero_grad()\n",
-    "    loss.backward()\n",
-    "    optimizer.step()"
-   ]
-  }
- ],
- "metadata": {
-  "interpreter": {
-   "hash": "fe38df673a99c62a9fea33a7aceda74c9b65b12ee9d076c5851d98b692a4989a"
-  },
-  "kernelspec": {
-   "display_name": "Python 3.7.9 64-bit ('py37': conda)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/codes/A2C/task0.py b/codes/A2C/task0.py
index bfea4d7..e29266b 100644
--- a/codes/A2C/task0.py
+++ b/codes/A2C/task0.py
@@ -29,14 +29,13 @@ def get_args():
     parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor")
     parser.add_argument('--lr',default=1e-3,type=float,help="learning rate")
     parser.add_argument('--hidden_dim',default=256,type=int)
+    parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") 
     parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
             '/' + curr_time + '/results/' )
     parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
             '/' + curr_time + '/models/' ) # path to save models
     parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")           
-    args = parser.parse_args()    
-    args.device = torch.device(
-            "cuda" if torch.cuda.is_available() else "cpu")  # check GPU                        
+    args = parser.parse_args()                        
     return args
 
 def make_envs(env_name):
@@ -124,14 +123,15 @@ def train(cfg,envs):
         loss.backward()
         optimizer.step()
     print('Finish training！')
-    return test_rewards, test_ma_rewards
+    return {'rewards':test_rewards,'ma_rewards':test_ma_rewards}
 if __name__ == "__main__":
     cfg = get_args()
     envs = [make_envs(cfg.env_name) for i in range(cfg.n_envs)]
     envs = SubprocVecEnv(envs) 
     # training
-    rewards,ma_rewards = train(cfg,envs)
+    res_dic = train(cfg,envs)
     make_dir(cfg.result_path,cfg.model_path)
     save_args(cfg)
-    save_results(rewards, ma_rewards, tag='train', path=cfg.result_path) # 保存结果
-    plot_rewards(rewards, ma_rewards, cfg, tag="train") # 画出结果
+    save_results(res_dic, tag='train',
+                 path=cfg.result_path)
+    plot_rewards(res_dic['rewards'], res_dic['ma_rewards'], cfg, tag="train") # 画出结果
diff --git a/codes/DDPG/ddpg.py b/codes/DDPG/ddpg.py
index 4d2ed42..93894e3 100644
--- a/codes/DDPG/ddpg.py
+++ b/codes/DDPG/ddpg.py
@@ -73,11 +73,11 @@ class Critic(nn.Module):
         return x
 class DDPG:
     def __init__(self, n_states, n_actions, cfg):
-        self.device = cfg.device
-        self.critic = Critic(n_states, n_actions, cfg.hidden_dim).to(cfg.device)
-        self.actor = Actor(n_states, n_actions, cfg.hidden_dim).to(cfg.device)
-        self.target_critic = Critic(n_states, n_actions, cfg.hidden_dim).to(cfg.device)
-        self.target_actor = Actor(n_states, n_actions, cfg.hidden_dim).to(cfg.device)
+        self.device = torch.device(cfg.device)
+        self.critic = Critic(n_states, n_actions, cfg.hidden_dim).to(self.device)
+        self.actor = Actor(n_states, n_actions, cfg.hidden_dim).to(self.device)
+        self.target_critic = Critic(n_states, n_actions, cfg.hidden_dim).to(self.device)
+        self.target_actor = Actor(n_states, n_actions, cfg.hidden_dim).to(self.device)
 
         # 复制参数到目标网络
         for target_param, param in zip(self.target_critic.parameters(), self.critic.parameters()):
diff --git a/codes/DDPG/outputs/Pendulum-v1/20220713-225402/results/params.json b/codes/DDPG/outputs/Pendulum-v1/20220713-225402/results/params.json
new file mode 100644
index 0000000..7d22454
--- /dev/null
+++ b/codes/DDPG/outputs/Pendulum-v1/20220713-225402/results/params.json
@@ -0,0 +1,18 @@
+{
+    "algo_name": "DDPG",
+    "env_name": "Pendulum-v1",
+    "train_eps": 300,
+    "test_eps": 20,
+    "gamma": 0.99,
+    "critic_lr": 0.001,
+    "actor_lr": 0.0001,
+    "memory_capacity": 8000,
+    "batch_size": 128,
+    "target_update": 2,
+    "soft_tau": 0.01,
+    "hidden_dim": 256,
+    "deivce": "cpu",
+    "result_path": "C:\\Users\\24438\\Desktop\\rl-tutorials/outputs/DDPG/outputs/Pendulum-v1/20220713-225402/results//",
+    "model_path": "C:\\Users\\24438\\Desktop\\rl-tutorials/outputs/DDPG/outputs/Pendulum-v1/20220713-225402/models/",
+    "save_fig": true
+}
\ No newline at end of file
diff --git a/codes/DDPG/outputs/Pendulum-v1/20220713-225402/results/params.txt b/codes/DDPG/outputs/Pendulum-v1/20220713-225402/results/params.txt
deleted file mode 100644
index 95a5a55..0000000
--- a/codes/DDPG/outputs/Pendulum-v1/20220713-225402/results/params.txt
+++ /dev/null
@@ -1,18 +0,0 @@
------------------- start ------------------
-algo_name : DDPG
-env_name : Pendulum-v1
-train_eps : 300
-test_eps : 20
-gamma : 0.99
-critic_lr : 0.001
-actor_lr : 0.0001
-memory_capacity : 8000
-batch_size : 128
-target_update : 2
-soft_tau : 0.01
-hidden_dim : 256
-result_path : c:\Users\24438\Desktop\rl-tutorials\codes\DDPG/outputs/Pendulum-v1/20220713-225402/results/
-model_path : c:\Users\24438\Desktop\rl-tutorials\codes\DDPG/outputs/Pendulum-v1/20220713-225402/models/
-save_fig : True
-device : cuda
-------------------- end -------------------
\ No newline at end of file
diff --git a/codes/DDPG/task0.py b/codes/DDPG/task0.py
index 861d7f3..20688d3 100644
--- a/codes/DDPG/task0.py
+++ b/codes/DDPG/task0.py
@@ -5,7 +5,7 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-11 20:58:21
 @LastEditor: John
-LastEditTime: 2022-07-13 22:53:11
+LastEditTime: 2022-07-21 21:51:34
 @Discription: 
 @Environment: python 3.7.7
 '''
@@ -41,14 +41,13 @@ def get_args():
     parser.add_argument('--target_update',default=2,type=int)
     parser.add_argument('--soft_tau',default=1e-2,type=float)
     parser.add_argument('--hidden_dim',default=256,type=int)
+    parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") 
     parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
             '/' + curr_time + '/results/' )
     parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
             '/' + curr_time + '/models/' ) # path to save models
-    parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")           
-    args = parser.parse_args()    
-    args.device = torch.device(
-            "cuda" if torch.cuda.is_available() else "cpu")  # check GPU                        
+    parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")   
+    args = parser.parse_args()                           
     return args
 
 def env_agent_config(cfg,seed=1):
@@ -87,7 +86,7 @@ def train(cfg, env, agent):
         else:
             ma_rewards.append(ep_reward)
     print('Finish training!')
-    return rewards, ma_rewards
+    return {'rewards':rewards,'ma_rewards':ma_rewards}
 
 def test(cfg, env, agent):
     print('Start testing')
@@ -112,21 +111,23 @@ def test(cfg, env, agent):
             ma_rewards.append(ep_reward)
         print(f"Epside:{i_ep+1}/{cfg.test_eps}, Reward:{ep_reward:.1f}")
     print('Finish testing!')
-    return rewards, ma_rewards
+    return {'rewards':rewards,'ma_rewards':ma_rewards}
 if __name__ == "__main__":
     cfg = get_args()
     # training
     env,agent = env_agent_config(cfg,seed=1)
-    rewards, ma_rewards = train(cfg, env, agent)
+    res_dic = train(cfg, env, agent)
     make_dir(cfg.result_path, cfg.model_path)
     save_args(cfg)
     agent.save(path=cfg.model_path)
-    save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
-    plot_rewards(rewards, ma_rewards, cfg, tag="train")  # 画出结果
+    save_results(res_dic, tag='train',
+                 path=cfg.result_path)  
+    plot_rewards(res_dic['rewards'], res_dic['ma_rewards'], cfg, tag="train")  
     # testing
     env,agent = env_agent_config(cfg,seed=10)
     agent.load(path=cfg.model_path)
-    rewards,ma_rewards = test(cfg,env,agent)
-    save_results(rewards,ma_rewards,tag = 'test',path = cfg.result_path)
-    plot_rewards(rewards, ma_rewards, cfg, tag="test")  # 画出结果
+    res_dic = test(cfg,env,agent)
+    save_results(res_dic, tag='test',
+                 path=cfg.result_path)  
+    plot_rewards(res_dic['rewards'], res_dic['ma_rewards'], cfg, tag="test")   
 
diff --git a/codes/DQN/dqn.py b/codes/DQN/dqn.py
index 0fa0d94..5ce5e1e 100644
--- a/codes/DQN/dqn.py
+++ b/codes/DQN/dqn.py
@@ -5,7 +5,7 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-12 00:50:49
 @LastEditor: John
-LastEditTime: 2022-07-13 00:08:18
+LastEditTime: 2022-07-20 23:57:16
 @Discription: 
 @Environment: python 3.7.7
 '''
@@ -64,8 +64,8 @@ class ReplayBuffer:
 class DQN:
     def __init__(self, n_states,n_actions,cfg):
 
-        self.n_actions = n_actions  # 总的动作个数
-        self.device = cfg.device  # 设备，cpu或gpu等
+        self.n_actions = n_actions  
+        self.device = torch.device(cfg.device)  # cpu or cuda
         self.gamma = cfg.gamma  # 奖励的折扣因子
         # e-greedy策略相关参数
         self.frame_idx = 0  # 用于epsilon的衰减计数
diff --git a/codes/DQN/outputs/CartPole-v0/20220713-211653/results/params.json b/codes/DQN/outputs/CartPole-v0/20220713-211653/results/params.json
new file mode 100644
index 0000000..3dfcdd4
--- /dev/null
+++ b/codes/DQN/outputs/CartPole-v0/20220713-211653/results/params.json
@@ -0,0 +1,19 @@
+{
+    "algo_name": "DQN",
+    "env_name": "CartPole-v0",
+    "train_eps": 200,
+    "test_eps": 20,
+    "gamma": 0.95,
+    "epsilon_start": 0.95,
+    "epsilon_end": 0.01,
+    "epsilon_decay": 500,
+    "lr": 0.0001,
+    "memory_capacity": 100000,
+    "batch_size": 64,
+    "target_update": 4,
+    "hidden_dim": 256,
+    "deivce": "cpu",
+    "result_path": "C:\\Users\\24438\\Desktop\\rl-tutorials/outputs/CartPole-v0/20220713-211653/results/",
+    "model_path": "C:\\Users\\24438\\Desktop\\rl-tutorials/outputs/CartPole-v0/20220713-211653/models/",
+    "save_fig": true
+}
\ No newline at end of file
diff --git a/codes/DQN/outputs/CartPole-v0/20220713-211653/results/params.txt b/codes/DQN/outputs/CartPole-v0/20220713-211653/results/params.txt
deleted file mode 100644
index 40eac02..0000000
--- a/codes/DQN/outputs/CartPole-v0/20220713-211653/results/params.txt
+++ /dev/null
@@ -1,19 +0,0 @@
------------------- start ------------------
-algo_name : DQN
-env_name : CartPole-v0
-train_eps : 200
-test_eps : 20
-gamma : 0.95
-epsilon_start : 0.95
-epsilon_end : 0.01
-epsilon_decay : 500
-lr : 0.0001
-memory_capacity : 100000
-batch_size : 64
-target_update : 4
-hidden_dim : 256
-result_path : C:\Users\24438\Desktop\rl-tutorials\codes\DQN/outputs/CartPole-v0/20220713-211653/results/
-model_path : C:\Users\24438\Desktop\rl-tutorials\codes\DQN/outputs/CartPole-v0/20220713-211653/models/
-save_fig : True
-device : cuda
-------------------- end -------------------
\ No newline at end of file
diff --git a/codes/DQN/task0.py b/codes/DQN/task0.py
index 9ccf26f..04344aa 100644
--- a/codes/DQN/task0.py
+++ b/codes/DQN/task0.py
@@ -1,19 +1,16 @@
-from lib2to3.pytree import type_repr
-import sys
-import os
-from parso import parse
+import sys,os
+curr_path = os.path.dirname(os.path.abspath(__file__))  # current path
+parent_path = os.path.dirname(curr_path)  # parent path
+sys.path.append(parent_path)  # add to system path
 import torch.nn as nn
 import torch.nn.functional as F
-curr_path = os.path.dirname(os.path.abspath(__file__))  # 当前文件所在绝对路径
-parent_path = os.path.dirname(curr_path)  # 父路径
-sys.path.append(parent_path)  # 添加路径到系统路径
 
 import gym
 import torch
 import datetime
 import numpy as np
 import argparse
-from common.utils import save_results_1, make_dir
+from common.utils import save_results, make_dir
 from common.utils import plot_rewards,save_args
 from dqn import DQN
 
@@ -35,14 +32,13 @@ def get_args():
     parser.add_argument('--batch_size',default=64,type=int)
     parser.add_argument('--target_update',default=4,type=int)
     parser.add_argument('--hidden_dim',default=256,type=int)
+    parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") 
     parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
             '/' + curr_time + '/results/' )
     parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
             '/' + curr_time + '/models/' ) # path to save models
     parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")           
-    args = parser.parse_args()    
-    args.device = torch.device(
-            "cuda" if torch.cuda.is_available() else "cpu")  # check GPU                        
+    args = parser.parse_args()                          
     return args
 
 def env_agent_config(cfg,seed=1):
@@ -99,8 +95,8 @@ def train(cfg, env, agent):
 
 
 def test(cfg, env, agent):
-    print('开始测试!')
-    print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')
+    print('Start testing!')
+    print(f'Env:{cfg.env_name}, A{cfg.algo_name}, 设备：{cfg.device}')
     ############# 由于测试不需要使用epsilon-greedy策略，所以相应的值设置为0 ###############
     cfg.epsilon_start = 0.0  # e-greedy策略中初始epsilon
     cfg.epsilon_end = 0.0  # e-greedy策略中的终止epsilon
@@ -127,7 +123,7 @@ def test(cfg, env, agent):
         else:
             ma_rewards.append(ep_reward)
         print(f'Episode：{i_ep+1}/{cfg.test_eps}, Reward:{ep_reward:.2f}, Step:{ep_step:.2f}')
-    print('完成测试！')
+    print('Finish testing')
     env.close()
     return {'rewards':rewards,'ma_rewards':ma_rewards,'steps':steps}
 
@@ -137,16 +133,16 @@ if __name__ == "__main__":
     # 训练
     env, agent = env_agent_config(cfg)
     res_dic = train(cfg, env, agent)
-    make_dir(cfg.result_path, cfg.model_path)  # 创建保存结果和模型路径的文件夹
-    save_args(cfg)
-    agent.save(path=cfg.model_path)  # 保存模型
-    save_results_1(res_dic, tag='train',
-                 path=cfg.result_path)  # 保存结果
-    plot_rewards(res_dic['rewards'], res_dic['ma_rewards'], cfg, tag="train")  # 画出结果
+    make_dir(cfg.result_path, cfg.model_path)  
+    save_args(cfg) # save parameters
+    agent.save(path=cfg.model_path)  # save model
+    save_results(res_dic, tag='train',
+                 path=cfg.result_path)  
+    plot_rewards(res_dic['rewards'], res_dic['ma_rewards'], cfg, tag="train")  
     # 测试
     env, agent = env_agent_config(cfg)
     agent.load(path=cfg.model_path)  # 导入模型
     res_dic = test(cfg, env, agent)
-    save_results_1(res_dic, tag='test',
+    save_results(res_dic, tag='test',
                  path=cfg.result_path)  # 保存结果
     plot_rewards(res_dic['rewards'], res_dic['ma_rewards'],cfg, tag="test")  # 画出结果
diff --git a/codes/DoubleDQN/double_dqn.py b/codes/DoubleDQN/double_dqn.py
index 8dbdc52..78642ea 100644
--- a/codes/DoubleDQN/double_dqn.py
+++ b/codes/DoubleDQN/double_dqn.py
@@ -5,7 +5,7 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-12 00:50:49
 @LastEditor: John
-LastEditTime: 2021-11-19 18:07:09
+LastEditTime: 2022-07-21 00:08:26
 @Discription: 
 @Environment: python 3.7.7
 '''
@@ -65,7 +65,7 @@ class MLP(nn.Module):
 class DoubleDQN:
     def __init__(self, n_states, n_actions, cfg):
         self.n_actions = n_actions  # 总的动作个数
-        self.device = cfg.device  # 设备，cpu或gpu等
+        self.device = torch.device(cfg.device)  # 设备，cpu或gpu等
         self.gamma = cfg.gamma
         # e-greedy策略相关参数
         self.actions_count = 0
@@ -88,8 +88,7 @@ class DoubleDQN:
         '''选择动作
         '''
         self.actions_count += 1
-        self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
-            math.exp(-1. * self.actions_count / self.epsilon_decay)
+        self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * math.exp(-1. * self.actions_count / self.epsilon_decay)
         if random.random() > self.epsilon:
             with torch.no_grad():
                 # 先转为张量便于丢给神经网络,state元素数据原本为float64
diff --git a/codes/DoubleDQN/outputs/CartPole-v0/20211229-145006/models/checkpoint.pth b/codes/DoubleDQN/outputs/CartPole-v0/20211229-145006/models/checkpoint.pth
deleted file mode 100644
index 2ec6bfd..0000000
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deleted file mode 100644
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deleted file mode 100644
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diff --git a/codes/DoubleDQN/outputs/CartPole-v0/20211229-145006/results/train_ma_rewards.npy b/codes/DoubleDQN/outputs/CartPole-v0/20211229-145006/results/train_ma_rewards.npy
deleted file mode 100644
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deleted file mode 100644
index cb9dbeb..0000000
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diff --git a/codes/DoubleDQN/outputs/CartPole-v0/20220721-215416/models/checkpoint.pth b/codes/DoubleDQN/outputs/CartPole-v0/20220721-215416/models/checkpoint.pth
new file mode 100644
index 0000000..2818144
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diff --git a/codes/DoubleDQN/outputs/CartPole-v0/20220721-215416/results/params.json b/codes/DoubleDQN/outputs/CartPole-v0/20220721-215416/results/params.json
new file mode 100644
index 0000000..abc1877
--- /dev/null
+++ b/codes/DoubleDQN/outputs/CartPole-v0/20220721-215416/results/params.json
@@ -0,0 +1,19 @@
+{
+    "algo_name": "DoubleDQN",
+    "env_name": "CartPole-v0",
+    "train_eps": 200,
+    "test_eps": 20,
+    "gamma": 0.99,
+    "epsilon_start": 0.95,
+    "epsilon_end": 0.01,
+    "epsilon_decay": 500,
+    "lr": 0.0001,
+    "memory_capacity": 100000,
+    "batch_size": 64,
+    "target_update": 2,
+    "hidden_dim": 256,
+    "device": "cuda",
+    "result_path": "C:\\Users\\24438\\Desktop\\rl-tutorials\\codes\\DoubleDQN/outputs/CartPole-v0/20220721-215416/results/",
+    "model_path": "C:\\Users\\24438\\Desktop\\rl-tutorials\\codes\\DoubleDQN/outputs/CartPole-v0/20220721-215416/models/",
+    "save_fig": true
+}
\ No newline at end of file
diff --git a/codes/DoubleDQN/outputs/CartPole-v0/20220721-215416/results/test_ma_rewards.npy b/codes/DoubleDQN/outputs/CartPole-v0/20220721-215416/results/test_ma_rewards.npy
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similarity index 51%
rename from codes/DoubleDQN/outputs/CartPole-v0/20211229-145006/results/train_rewards.npy
rename to codes/DoubleDQN/outputs/CartPole-v0/20220721-215416/results/train_rewards.npy
index 3e707c5..d9b5730 100644
Binary files a/codes/DoubleDQN/outputs/CartPole-v0/20211229-145006/results/train_rewards.npy and b/codes/DoubleDQN/outputs/CartPole-v0/20220721-215416/results/train_rewards.npy differ
diff --git a/codes/DoubleDQN/outputs/CartPole-v0/20220721-215416/results/train_rewards_curve.png b/codes/DoubleDQN/outputs/CartPole-v0/20220721-215416/results/train_rewards_curve.png
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index 0000000..d07d996
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diff --git a/codes/DoubleDQN/task0.py b/codes/DoubleDQN/task0.py
index 2f91e1e..66dfcd9 100644
--- a/codes/DoubleDQN/task0.py
+++ b/codes/DoubleDQN/task0.py
@@ -5,55 +5,49 @@ Author: JiangJi
 Email: johnjim0816@gmail.com
 Date: 2021-11-07 18:10:37
 LastEditor: JiangJi
-LastEditTime: 2021-12-29 15:02:30
+LastEditTime: 2022-07-21 21:52:31
 Discription: 
 '''
-
 import sys,os
-curr_path = os.path.dirname(os.path.abspath(__file__)) # 当前文件所在绝对路径
-parent_path = os.path.dirname(curr_path) # 父路径
-sys.path.append(parent_path) # 添加路径到系统路径
+curr_path = os.path.dirname(os.path.abspath(__file__))  # current path
+parent_path = os.path.dirname(curr_path)  # parent path
+sys.path.append(parent_path)  # add to system path
 
 import gym
 import torch
 import datetime
+import argparse
 
-from common.utils import save_results, make_dir
-from common.utils import plot_rewards
+from common.utils import save_results,make_dir
+from common.utils import plot_rewards,save_args
 from DoubleDQN.double_dqn import DoubleDQN
 
-curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # 获取当前时间
-
-class Config:
-    def __init__(self):
-        ################################## 环境超参数 ###################################
-        self.algo_name = 'DoubleDQN' # 算法名称
-        self.env_name = 'CartPole-v0'  # 环境名称
-        self.device = torch.device(
-            "cuda" if torch.cuda.is_available() else "cpu")  # 检测GPU
-        self.train_eps = 200  # 训练的回合数
-        self.test_eps = 30  # 测试的回合数
-        ################################################################################
-
-        ################################## 算法超参数 ###################################
-        self.gamma = 0.95  # 强化学习中的折扣因子
-        self.epsilon_start = 0.95  # e-greedy策略中初始epsilon
-        self.epsilon_end = 0.01  # e-greedy策略中的终止epsilon
-        self.epsilon_decay = 500  # e-greedy策略中epsilon的衰减率
-        self.lr = 0.0001  # 学习率
-        self.memory_capacity = 100000  # 经验回放的容量
-        self.batch_size = 64  # mini-batch SGD中的批量大小
-        self.target_update = 2  # 目标网络的更新频率
-        self.hidden_dim = 256  # 网络隐藏层
-        ################################################################################
-
-        ################################# 保存结果相关参数 ##############################
-        self.result_path = curr_path + "/outputs/" + self.env_name + \
-            '/' + curr_time + '/results/'  # 保存结果的路径
-        self.model_path = curr_path + "/outputs/" + self.env_name + \
-            '/' + curr_time + '/models/'  # 保存模型的路径
-        self.save = True # 是否保存图片
-        ################################################################################
+def get_args():
+    """ Hyperparameters
+    """
+    curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # Obtain current time
+    parser = argparse.ArgumentParser(description="hyperparameters")      
+    parser.add_argument('--algo_name',default='DoubleDQN',type=str,help="name of algorithm")
+    parser.add_argument('--env_name',default='CartPole-v0',type=str,help="name of environment")
+    parser.add_argument('--train_eps',default=200,type=int,help="episodes of training")
+    parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing")
+    parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor")
+    parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon")
+    parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon")
+    parser.add_argument('--epsilon_decay',default=500,type=int,help="decay rate of epsilon")
+    parser.add_argument('--lr',default=0.0001,type=float,help="learning rate")
+    parser.add_argument('--memory_capacity',default=100000,type=int,help="memory capacity")
+    parser.add_argument('--batch_size',default=64,type=int)
+    parser.add_argument('--target_update',default=2,type=int)
+    parser.add_argument('--hidden_dim',default=256,type=int)
+    parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") 
+    parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
+            '/' + curr_time + '/results/' )
+    parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
+            '/' + curr_time + '/models/' ) # path to save models
+    parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")           
+    args = parser.parse_args()                          
+    return args
         
         
 def env_agent_config(cfg,seed=1):
@@ -65,8 +59,8 @@ def env_agent_config(cfg,seed=1):
     return env,agent
 
 def train(cfg,env,agent):
-    print('开始训练!')
-    print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')
+    print('Start training!')
+    print(f'Env:{cfg.env_name}, Algorithm:{cfg.algo_name}, Device:{cfg.device}')
     rewards = [] # 记录所有回合的奖励
     ma_rewards = []  # 记录所有回合的滑动平均奖励
     for i_ep in range(cfg.train_eps):
@@ -84,20 +78,19 @@ def train(cfg,env,agent):
         if i_ep % cfg.target_update == 0:
             agent.target_net.load_state_dict(agent.policy_net.state_dict())
         if (i_ep+1)%10 == 0: 
-            print(f'回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward}')
+            print(f'Env:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.2f}')
         rewards.append(ep_reward)
         if ma_rewards:
             ma_rewards.append(
                 0.9*ma_rewards[-1]+0.1*ep_reward)
         else:
             ma_rewards.append(ep_reward)   
-    print('完成训练！')
-    env.close()
-    return rewards,ma_rewards
+    print('Finish training!')
+    return {'rewards':rewards,'ma_rewards':ma_rewards}
 
 def test(cfg,env,agent):
-    print('开始测试!')
-    print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')
+    print('Start testing')
+    print(f'Env:{cfg.env_name}, Algorithm:{cfg.algo_name}, Device:{cfg.device}')
     ############# 由于测试不需要使用epsilon-greedy策略，所以相应的值设置为0 ###############
     cfg.epsilon_start = 0.0  # e-greedy策略中初始epsilon
     cfg.epsilon_end = 0.0  # e-greedy策略中的终止epsilon
@@ -120,25 +113,26 @@ def test(cfg,env,agent):
             ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
         else:
             ma_rewards.append(ep_reward)
-        print(f"回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.1f}")
-    print('完成测试！')
-    env.close()
-    return rewards,ma_rewards  
+        print(f"Epside:{i_ep+1}/{cfg.test_eps}, Reward:{ep_reward:.1f}")
+    print('Finish testing!')
+    return {'rewards':rewards,'ma_rewards':ma_rewards}
 
 if __name__ == "__main__":
-    cfg = Config()
-    # 训练
-    env, agent = env_agent_config(cfg)
-    rewards, ma_rewards = train(cfg, env, agent)
-    make_dir(cfg.result_path, cfg.model_path)  # 创建保存结果和模型路径的文件夹
-    agent.save(path=cfg.model_path)  # 保存模型
-    save_results(rewards, ma_rewards, tag='train',
-                 path=cfg.result_path)  # 保存结果
-    plot_rewards(rewards, ma_rewards, cfg, tag="train")  # 画出结果
-    # 测试
-    env, agent = env_agent_config(cfg)
-    agent.load(path=cfg.model_path)  # 导入模型
-    rewards, ma_rewards = test(cfg, env, agent)
-    save_results(rewards, ma_rewards, tag='test',
-                 path=cfg.result_path)  # 保存结果
-    plot_rewards(rewards, ma_rewards, cfg, tag="test")  # 画出结果
+    cfg = get_args()
+    print(cfg.device)
+    # training
+    env,agent = env_agent_config(cfg,seed=1)
+    res_dic = train(cfg, env, agent)
+    make_dir(cfg.result_path, cfg.model_path)
+    save_args(cfg)
+    agent.save(path=cfg.model_path)
+    save_results(res_dic, tag='train',
+                 path=cfg.result_path)  
+    plot_rewards(res_dic['rewards'], res_dic['ma_rewards'], cfg, tag="train")  
+    # testing
+    env,agent = env_agent_config(cfg,seed=10)
+    agent.load(path=cfg.model_path)
+    res_dic = test(cfg,env,agent)
+    save_results(res_dic, tag='test',
+                 path=cfg.result_path)  
+    plot_rewards(res_dic['rewards'], res_dic['ma_rewards'], cfg, tag="test") 
diff --git a/codes/PPO/task0.py b/codes/PPO/task0.py
index 2d40944..6a73ff8 100644
--- a/codes/PPO/task0.py
+++ b/codes/PPO/task0.py
@@ -16,7 +16,7 @@ curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # 获取当前时
 class Config:
     def __init__(self) -> None:
         ################################## 环境超参数 ###################################
-        self.algo_name = "DQN"  # 算法名称
+        self.algo_name = "PPO"  # 算法名称
         self.env_name = 'CartPole-v0' # 环境名称
         self.continuous = False # 环境是否为连续动作
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  # 检测GPU
diff --git a/codes/PolicyGradient/task0.py b/codes/PolicyGradient/task0.py
index c676fe3..b9e11a0 100644
--- a/codes/PolicyGradient/task0.py
+++ b/codes/PolicyGradient/task0.py
@@ -5,56 +5,47 @@ Author: John
 Email: johnjim0816@gmail.com
 Date: 2020-11-22 23:21:53
 LastEditor: John
-LastEditTime: 2022-02-10 06:13:21
+LastEditTime: 2022-07-21 21:44:00
 Discription: 
 Environment: 
 '''
-import sys
-import os
-curr_path = os.path.dirname(os.path.abspath(__file__))  # 当前文件所在绝对路径
-parent_path = os.path.dirname(curr_path)  # 父路径
-sys.path.append(parent_path)  # 添加路径到系统路径
+import sys,os
+curr_path = os.path.dirname(os.path.abspath(__file__))  # current path
+parent_path = os.path.dirname(curr_path)  # parent path
+sys.path.append(parent_path)  # add to system path
 
 import gym
 import torch
 import datetime
+import argparse
 from itertools import count
 
 from pg import PolicyGradient
 from common.utils import save_results, make_dir
 from common.utils import plot_rewards
 
-curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # 获取当前时间
 
-class Config:
-    '''超参数
-    '''
-
-    def __init__(self):
-        ################################## 环境超参数 ###################################
-        self.algo_name = "PolicyGradient"  # 算法名称
-        self.env_name = 'CartPole-v0' # 环境名称
-        self.device = torch.device(
-            "cuda" if torch.cuda.is_available() else "cpu")  # 检测GPUgjgjlkhfsf风刀霜的撒发十
-        self.seed = 10 # 随机种子，置0则不设置随机种子
-        self.train_eps = 300 # 训练的回合数
-        self.test_eps = 30 # 测试的回合数
-        ################################################################################
-        
-        ################################## 算法超参数 ###################################
-        self.batch_size = 8 # mini-batch SGD中的批量大小
-        self.lr = 0.01 # 学习率
-        self.gamma = 0.99 # 强化学习中的折扣因子
-        self.hidden_dim = 36 # 网络隐藏层
-        ################################################################################
-        
-        ################################# 保存结果相关参数 ################################
-        self.result_path = curr_path + "/outputs/" + self.env_name + \
-            '/' + curr_time + '/results/'  # 保存结果的路径
-        self.model_path = curr_path + "/outputs/" + self.env_name + \
-            '/' + curr_time + '/models/'  # 保存模型的路径
-        self.save = True # 是否保存图片
-        ################################################################################
+def get_args():
+    """ Hyperparameters
+    """
+    curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # Obtain current time
+    parser = argparse.ArgumentParser(description="hyperparameters")      
+    parser.add_argument('--algo_name',default='PolicyGradient',type=str,help="name of algorithm")
+    parser.add_argument('--env_name',default='CartPole-v0',type=str,help="name of environment")
+    parser.add_argument('--train_eps',default=300,type=int,help="episodes of training")
+    parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing")
+    parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor")
+    parser.add_argument('--lr',default=0.01,type=float,help="learning rate")
+    parser.add_argument('--batch_size',default=8,type=int)
+    parser.add_argument('--hidden_dim',default=36,type=int)
+    parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") 
+    parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
+            '/' + curr_time + '/results/' )
+    parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
+            '/' + curr_time + '/models/' ) # path to save models
+    parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")           
+    args = parser.parse_args()                          
+    return args
 
 
 def env_agent_config(cfg,seed=1):
@@ -65,9 +56,9 @@ def env_agent_config(cfg,seed=1):
     return env,agent
 
 def train(cfg,env,agent):
-    print('开始训练!')
-    print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')
-    state_pool = [] # 存放每batch_size个episode的state序列
+    print('Start training!')
+    print(f'Env:{cfg.env_name}, Algorithm:{cfg.algo_name}, Device:{cfg.device}')
+    state_pool = [] # temp states pool per several episodes
     action_pool = []
     reward_pool = [] 
     rewards = []
@@ -86,11 +77,11 @@ def train(cfg,env,agent):
             reward_pool.append(reward)
             state = next_state
             if done:
-                print('回合：{}/{}, 奖励：{}'.format(i_ep + 1, cfg.train_eps, ep_reward))
+                print(f'Episode：{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.2f}')
                 break
         if i_ep > 0 and i_ep % cfg.batch_size == 0:
             agent.update(reward_pool,state_pool,action_pool)
-            state_pool = [] # 每个episode的state
+            state_pool = [] 
             action_pool = []
             reward_pool = []
         rewards.append(ep_reward)
@@ -99,8 +90,8 @@ def train(cfg,env,agent):
                 0.9*ma_rewards[-1]+0.1*ep_reward)
         else:
             ma_rewards.append(ep_reward)
-    print('完成训练！')
-    env.close()
+    print('Finish training!')
+    env.close() # close environment
     return rewards, ma_rewards
             
 
diff --git a/codes/common/utils.py b/codes/common/utils.py
index b47ef72..654b73c 100644
--- a/codes/common/utils.py
+++ b/codes/common/utils.py
@@ -5,7 +5,7 @@ Author: John
 Email: johnjim0816@gmail.com
 Date: 2021-03-12 16:02:24
 LastEditor: John
-LastEditTime: 2022-07-13 22:15:46
+LastEditTime: 2022-07-21 21:45:33
 Discription: 
 Environment: 
 '''
@@ -14,6 +14,7 @@ import numpy as np
 from pathlib import Path
 import matplotlib.pyplot as plt
 import seaborn as sns
+import json
 
 from matplotlib.font_manager import FontProperties  # 导入字体模块
 
@@ -68,19 +69,19 @@ def plot_losses(losses, algo="DQN", save=True, path='./'):
         plt.savefig(path+"losses_curve")
     plt.show()
 
-def save_results_1(dic, tag='train', path='./results'):
+def save_results(dic, tag='train', path='./results'):
     ''' 保存奖励
     '''
     for key,value in dic.items():
         np.save(path+'{}_{}.npy'.format(tag,key),value)
     print('Results saved！')
     
-def save_results(rewards, ma_rewards, tag='train', path='./results'):
-    ''' 保存奖励
-    '''
-    np.save(path+'{}_rewards.npy'.format(tag), rewards)
-    np.save(path+'{}_ma_rewards.npy'.format(tag), ma_rewards)
-    print('Result saved!')
+# def save_results(rewards, ma_rewards, tag='train', path='./results'):
+#     ''' 保存奖励
+#     '''
+#     np.save(path+'{}_rewards.npy'.format(tag), rewards)
+#     np.save(path+'{}_ma_rewards.npy'.format(tag), ma_rewards)
+#     print('Result saved!')
 
 
 def make_dir(*paths):
@@ -101,11 +102,8 @@ def del_empty_dir(*paths):
 
 def save_args(args):
     # save parameters    
-    argsDict = args.__dict__
-    with open(args.result_path+'params.txt', 'w') as f:
-        f.writelines('------------------ start ------------------' + '\n')
-        for eachArg, value in argsDict.items():
-            f.writelines(eachArg + ' : ' + str(value) + '\n')
-        f.writelines('------------------- end -------------------')     
+    args_dict = vars(args)   
+    with open(args.result_path+'params.json', 'w') as fp:
+        json.dump(args_dict, fp)   
     print("Parameters saved!")
     
\ No newline at end of file
diff --git a/notebooks/A2C.ipynb b/notebooks/A2C.ipynb
new file mode 100644
index 0000000..8966eac
--- /dev/null
+++ b/notebooks/A2C.ipynb
@@ -0,0 +1,370 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.optim as optim\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "from torch.distributions import Categorical\n",
+    "import numpy as np\n",
+    "from multiprocessing import Process, Pipe\n",
+    "import argparse\n",
+    "import gym"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 建立Actor和Critic网络"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ActorCritic(nn.Module):\n",
+    "    ''' A2C网络模型，包含一个Actor和Critic\n",
+    "    '''\n",
+    "    def __init__(self, input_dim, output_dim, hidden_dim):\n",
+    "        super(ActorCritic, self).__init__()\n",
+    "        self.critic = nn.Sequential(\n",
+    "            nn.Linear(input_dim, hidden_dim),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Linear(hidden_dim, 1)\n",
+    "        )\n",
+    "        \n",
+    "        self.actor = nn.Sequential(\n",
+    "            nn.Linear(input_dim, hidden_dim),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Linear(hidden_dim, output_dim),\n",
+    "            nn.Softmax(dim=1),\n",
+    "        )\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        value = self.critic(x)\n",
+    "        probs = self.actor(x)\n",
+    "        dist  = Categorical(probs)\n",
+    "        return dist, value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class A2C:\n",
+    "    ''' A2C算法\n",
+    "    '''\n",
+    "    def __init__(self,n_states,n_actions,cfg) -> None:\n",
+    "        self.gamma = cfg.gamma\n",
+    "        self.device = cfg.device\n",
+    "        self.model = ActorCritic(n_states, n_actions, cfg.hidden_size).to(self.device)\n",
+    "        self.optimizer = optim.Adam(self.model.parameters())\n",
+    "\n",
+    "    def compute_returns(self,next_value, rewards, masks):\n",
+    "        R = next_value\n",
+    "        returns = []\n",
+    "        for step in reversed(range(len(rewards))):\n",
+    "            R = rewards[step] + self.gamma * R * masks[step]\n",
+    "            returns.insert(0, R)\n",
+    "        return returns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_envs(env_name):\n",
+    "    def _thunk():\n",
+    "        env = gym.make(env_name)\n",
+    "        env.seed(2)\n",
+    "        return env\n",
+    "    return _thunk\n",
+    "def test_env(env,model,vis=False):\n",
+    "    state = env.reset()\n",
+    "    if vis: env.render()\n",
+    "    done = False\n",
+    "    total_reward = 0\n",
+    "    while not done:\n",
+    "        state = torch.FloatTensor(state).unsqueeze(0).to(cfg.device)\n",
+    "        dist, _ = model(state)\n",
+    "        next_state, reward, done, _ = env.step(dist.sample().cpu().numpy()[0])\n",
+    "        state = next_state\n",
+    "        if vis: env.render()\n",
+    "        total_reward += reward\n",
+    "    return total_reward\n",
+    "\n",
+    "def compute_returns(next_value, rewards, masks, gamma=0.99):\n",
+    "    R = next_value\n",
+    "    returns = []\n",
+    "    for step in reversed(range(len(rewards))):\n",
+    "        R = rewards[step] + gamma * R * masks[step]\n",
+    "        returns.insert(0, R)\n",
+    "    return returns\n",
+    "\n",
+    "\n",
+    "def train(cfg,envs):\n",
+    "    print('Start training!')\n",
+    "    print(f'Env:{cfg.env_name}, Algorithm:{cfg.algo_name}, Device:{cfg.device}')\n",
+    "    env = gym.make(cfg.env_name) # a single env\n",
+    "    env.seed(10)\n",
+    "    n_states  = envs.observation_space.shape[0]\n",
+    "    n_actions = envs.action_space.n\n",
+    "    model = ActorCritic(n_states, n_actions, cfg.hidden_dim).to(cfg.device)\n",
+    "    optimizer = optim.Adam(model.parameters())\n",
+    "    step_idx    = 0\n",
+    "    test_rewards = []\n",
+    "    test_ma_rewards = []\n",
+    "    state = envs.reset()\n",
+    "    while step_idx < cfg.max_steps:\n",
+    "        log_probs = []\n",
+    "        values    = []\n",
+    "        rewards   = []\n",
+    "        masks     = []\n",
+    "        entropy = 0\n",
+    "        # rollout trajectory\n",
+    "        for _ in range(cfg.n_steps):\n",
+    "            state = torch.FloatTensor(state).to(cfg.device)\n",
+    "            dist, value = model(state)\n",
+    "            action = dist.sample()\n",
+    "            next_state, reward, done, _ = envs.step(action.cpu().numpy())\n",
+    "            log_prob = dist.log_prob(action)\n",
+    "            entropy += dist.entropy().mean()\n",
+    "            log_probs.append(log_prob)\n",
+    "            values.append(value)\n",
+    "            rewards.append(torch.FloatTensor(reward).unsqueeze(1).to(cfg.device))\n",
+    "            masks.append(torch.FloatTensor(1 - done).unsqueeze(1).to(cfg.device))\n",
+    "            state = next_state\n",
+    "            step_idx += 1\n",
+    "            if step_idx % 200 == 0:\n",
+    "                test_reward = np.mean([test_env(env,model) for _ in range(10)])\n",
+    "                print(f\"step_idx:{step_idx}, test_reward:{test_reward}\")\n",
+    "                test_rewards.append(test_reward)\n",
+    "                if test_ma_rewards:\n",
+    "                    test_ma_rewards.append(0.9*test_ma_rewards[-1]+0.1*test_reward)\n",
+    "                else:\n",
+    "                    test_ma_rewards.append(test_reward) \n",
+    "                # plot(step_idx, test_rewards)   \n",
+    "        next_state = torch.FloatTensor(next_state).to(cfg.device)\n",
+    "        _, next_value = model(next_state)\n",
+    "        returns = compute_returns(next_value, rewards, masks)\n",
+    "        log_probs = torch.cat(log_probs)\n",
+    "        returns   = torch.cat(returns).detach()\n",
+    "        values    = torch.cat(values)\n",
+    "        advantage = returns - values\n",
+    "        actor_loss  = -(log_probs * advantage.detach()).mean()\n",
+    "        critic_loss = advantage.pow(2).mean()\n",
+    "        loss = actor_loss + 0.5 * critic_loss - 0.001 * entropy\n",
+    "        optimizer.zero_grad()\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "    print('Finish training！')\n",
+    "    return test_rewards, test_ma_rewards"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns \n",
+    "def plot_rewards(rewards, ma_rewards, cfg, tag='train'):\n",
+    "    sns.set()\n",
+    "    plt.figure()  # 创建一个图形实例，方便同时多画几个图\n",
+    "    plt.title(\"learning curve on {} of {} for {}\".format(\n",
+    "        cfg.device, cfg.algo_name, cfg.env_name))\n",
+    "    plt.xlabel('epsiodes')\n",
+    "    plt.plot(rewards, label='rewards')\n",
+    "    plt.plot(ma_rewards, label='ma rewards')\n",
+    "    plt.legend()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Start training!\n",
+      "Env:CartPole-v0, Algorithm:A2C, Device:cuda\n",
+      "step_idx:200, test_reward:18.6\n",
+      "step_idx:400, test_reward:19.7\n",
+      "step_idx:600, test_reward:24.2\n",
+      "step_idx:800, test_reward:19.5\n",
+      "step_idx:1000, test_reward:33.9\n",
+      "step_idx:1200, test_reward:36.1\n",
+      "step_idx:1400, test_reward:32.6\n",
+      "step_idx:1600, test_reward:36.3\n",
+      "step_idx:1800, test_reward:38.9\n",
+      "step_idx:2000, test_reward:60.8\n",
+      "step_idx:2200, test_reward:41.9\n",
+      "step_idx:2400, test_reward:42.2\n",
+      "step_idx:2600, test_reward:71.6\n",
+      "step_idx:2800, test_reward:123.6\n",
+      "step_idx:3000, test_reward:57.5\n",
+      "step_idx:3200, test_reward:155.4\n",
+      "step_idx:3400, test_reward:111.4\n",
+      "step_idx:3600, test_reward:133.8\n",
+      "step_idx:3800, test_reward:133.8\n",
+      "step_idx:4000, test_reward:114.3\n",
+      "step_idx:4200, test_reward:165.5\n",
+      "step_idx:4400, test_reward:119.4\n",
+      "step_idx:4600, test_reward:173.4\n",
+      "step_idx:4800, test_reward:115.4\n",
+      "step_idx:5000, test_reward:159.7\n",
+      "step_idx:5200, test_reward:178.1\n",
+      "step_idx:5400, test_reward:137.8\n",
+      "step_idx:5600, test_reward:146.0\n",
+      "step_idx:5800, test_reward:187.4\n",
+      "step_idx:6000, test_reward:200.0\n",
+      "step_idx:6200, test_reward:169.2\n",
+      "step_idx:6400, test_reward:167.8\n",
+      "step_idx:6600, test_reward:184.3\n",
+      "step_idx:6800, test_reward:162.3\n",
+      "step_idx:7000, test_reward:125.4\n",
+      "step_idx:7200, test_reward:150.6\n",
+      "step_idx:7400, test_reward:152.6\n",
+      "step_idx:7600, test_reward:122.5\n",
+      "step_idx:7800, test_reward:136.3\n",
+      "step_idx:8000, test_reward:131.4\n",
+      "step_idx:8200, test_reward:174.6\n",
+      "step_idx:8400, test_reward:91.7\n",
+      "step_idx:8600, test_reward:170.1\n",
+      "step_idx:8800, test_reward:166.0\n",
+      "step_idx:9000, test_reward:150.2\n",
+      "step_idx:9200, test_reward:104.6\n",
+      "step_idx:9400, test_reward:147.2\n",
+      "step_idx:9600, test_reward:111.8\n",
+      "step_idx:9800, test_reward:118.7\n",
+      "step_idx:10000, test_reward:102.6\n",
+      "step_idx:10200, test_reward:99.0\n",
+      "step_idx:10400, test_reward:64.6\n",
+      "step_idx:10600, test_reward:133.7\n",
+      "step_idx:10800, test_reward:119.7\n",
+      "step_idx:11000, test_reward:112.6\n",
+      "step_idx:11200, test_reward:116.1\n",
+      "step_idx:11400, test_reward:116.3\n",
+      "step_idx:11600, test_reward:116.2\n",
+      "step_idx:11800, test_reward:115.3\n",
+      "step_idx:12000, test_reward:109.7\n",
+      "step_idx:12200, test_reward:110.3\n",
+      "step_idx:12400, test_reward:131.4\n",
+      "step_idx:12600, test_reward:128.3\n",
+      "step_idx:12800, test_reward:128.8\n",
+      "step_idx:13000, test_reward:119.8\n",
+      "step_idx:13200, test_reward:108.6\n",
+      "step_idx:13400, test_reward:128.4\n",
+      "step_idx:13600, test_reward:138.2\n",
+      "step_idx:13800, test_reward:119.1\n",
+      "step_idx:14000, test_reward:140.7\n",
+      "step_idx:14200, test_reward:145.3\n",
+      "step_idx:14400, test_reward:154.1\n",
+      "step_idx:14600, test_reward:165.2\n",
+      "step_idx:14800, test_reward:138.2\n",
+      "step_idx:15000, test_reward:143.5\n",
+      "step_idx:15200, test_reward:125.4\n",
+      "step_idx:15400, test_reward:137.1\n",
+      "step_idx:15600, test_reward:150.1\n",
+      "step_idx:15800, test_reward:132.9\n",
+      "step_idx:16000, test_reward:140.4\n",
+      "step_idx:16200, test_reward:141.3\n",
+      "step_idx:16400, test_reward:135.5\n",
+      "step_idx:16600, test_reward:135.5\n",
+      "step_idx:16800, test_reward:125.6\n",
+      "step_idx:17000, test_reward:126.8\n",
+      "step_idx:17200, test_reward:124.7\n",
+      "step_idx:17400, test_reward:129.6\n",
+      "step_idx:17600, test_reward:114.3\n",
+      "step_idx:17800, test_reward:57.3\n",
+      "step_idx:18000, test_reward:164.7\n",
+      "step_idx:18200, test_reward:165.8\n",
+      "step_idx:18400, test_reward:196.7\n",
+      "step_idx:18600, test_reward:198.8\n",
+      "step_idx:18800, test_reward:200.0\n",
+      "step_idx:19000, test_reward:199.6\n",
+      "step_idx:19200, test_reward:189.5\n",
+      "step_idx:19400, test_reward:177.9\n",
+      "step_idx:19600, test_reward:159.3\n",
+      "step_idx:19800, test_reward:127.7\n",
+      "step_idx:20000, test_reward:143.6\n",
+      "Finish training！\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import easydict\n",
+    "from common.multiprocessing_env import SubprocVecEnv\n",
+    "cfg = easydict.EasyDict({\n",
+    "        \"algo_name\": 'A2C',\n",
+    "        \"env_name\": 'CartPole-v0',\n",
+    "        \"n_envs\": 8,\n",
+    "        \"max_steps\": 20000,\n",
+    "        \"n_steps\":5,\n",
+    "        \"gamma\":0.99,\n",
+    "        \"lr\": 1e-3,\n",
+    "        \"hidden_dim\": 256,\n",
+    "        \"device\":torch.device(\n",
+    "            \"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "})\n",
+    "envs = [make_envs(cfg.env_name) for i in range(cfg.n_envs)]\n",
+    "envs = SubprocVecEnv(envs) \n",
+    "rewards,ma_rewards = train(cfg,envs)\n",
+    "plot_rewards(rewards, ma_rewards, cfg, tag=\"train\") # 画出结果"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.12 ('rl_tutorials')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.12"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "4f613f1ab80ec98dc1b91d6e720de51301598a187317378e53e49b773c1123dd"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/common/multiprocessing_env.py b/notebooks/common/multiprocessing_env.py
new file mode 100644
index 0000000..28c8aba
--- /dev/null
+++ b/notebooks/common/multiprocessing_env.py
@@ -0,0 +1,153 @@
+# 该代码来自 openai baseline，用于多线程环境
+# https://github.com/openai/baselines/tree/master/baselines/common/vec_env
+
+import numpy as np
+from multiprocessing import Process, Pipe
+
+def worker(remote, parent_remote, env_fn_wrapper):
+    parent_remote.close()
+    env = env_fn_wrapper.x()
+    while True:
+        cmd, data = remote.recv()
+        if cmd == 'step':
+            ob, reward, done, info = env.step(data)
+            if done:
+                ob = env.reset()
+            remote.send((ob, reward, done, info))
+        elif cmd == 'reset':
+            ob = env.reset()
+            remote.send(ob)
+        elif cmd == 'reset_task':
+            ob = env.reset_task()
+            remote.send(ob)
+        elif cmd == 'close':
+            remote.close()
+            break
+        elif cmd == 'get_spaces':
+            remote.send((env.observation_space, env.action_space))
+        else:
+            raise NotImplementedError
+
+class VecEnv(object):
+    """
+    An abstract asynchronous, vectorized environment.
+    """
+    def __init__(self, num_envs, observation_space, action_space):
+        self.num_envs = num_envs
+        self.observation_space = observation_space
+        self.action_space = action_space
+
+    def reset(self):
+        """
+        Reset all the environments and return an array of
+        observations, or a tuple of observation arrays.
+        If step_async is still doing work, that work will
+        be cancelled and step_wait() should not be called
+        until step_async() is invoked again.
+        """
+        pass
+
+    def step_async(self, actions):
+        """
+        Tell all the environments to start taking a step
+        with the given actions.
+        Call step_wait() to get the results of the step.
+        You should not call this if a step_async run is
+        already pending.
+        """
+        pass
+
+    def step_wait(self):
+        """
+        Wait for the step taken with step_async().
+        Returns (obs, rews, dones, infos):
+         - obs: an array of observations, or a tuple of
+                arrays of observations.
+         - rews: an array of rewards
+         - dones: an array of "episode done" booleans
+         - infos: a sequence of info objects
+        """
+        pass
+
+    def close(self):
+        """
+        Clean up the environments' resources.
+        """
+        pass
+
+    def step(self, actions):
+        self.step_async(actions)
+        return self.step_wait()
+
+    
+class CloudpickleWrapper(object):
+    """
+    Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle)
+    """
+    def __init__(self, x):
+        self.x = x
+    def __getstate__(self):
+        import cloudpickle
+        return cloudpickle.dumps(self.x)
+    def __setstate__(self, ob):
+        import pickle
+        self.x = pickle.loads(ob)
+
+        
+class SubprocVecEnv(VecEnv):
+    def __init__(self, env_fns, spaces=None):
+        """
+        envs: list of gym environments to run in subprocesses
+        """
+        self.waiting = False
+        self.closed = False
+        nenvs = len(env_fns)
+        self.nenvs = nenvs
+        self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)])
+        self.ps = [Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))
+            for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)]
+        for p in self.ps:
+            p.daemon = True # if the main process crashes, we should not cause things to hang
+            p.start()
+        for remote in self.work_remotes:
+            remote.close()
+
+        self.remotes[0].send(('get_spaces', None))
+        observation_space, action_space = self.remotes[0].recv()
+        VecEnv.__init__(self, len(env_fns), observation_space, action_space)
+
+    def step_async(self, actions):
+        for remote, action in zip(self.remotes, actions):
+            remote.send(('step', action))
+        self.waiting = True
+
+    def step_wait(self):
+        results = [remote.recv() for remote in self.remotes]
+        self.waiting = False
+        obs, rews, dones, infos = zip(*results)
+        return np.stack(obs), np.stack(rews), np.stack(dones), infos
+
+    def reset(self):
+        for remote in self.remotes:
+            remote.send(('reset', None))
+        return np.stack([remote.recv() for remote in self.remotes])
+
+    def reset_task(self):
+        for remote in self.remotes:
+            remote.send(('reset_task', None))
+        return np.stack([remote.recv() for remote in self.remotes])
+
+    def close(self):
+        if self.closed:
+            return
+        if self.waiting:
+            for remote in self.remotes:            
+                remote.recv()
+        for remote in self.remotes:
+            remote.send(('close', None))
+        for p in self.ps:
+            p.join()
+            self.closed = True
+            
+    def __len__(self):
+        return self.nenvs
\ No newline at end of file