update rainbowdqn

codes/PolicyGradient/model.py

@@ -1,31 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-Author: John
-Email: johnjim0816@gmail.com
-Date: 2021-03-23 16:35:58
-LastEditor: John
-LastEditTime: 2021-12-21 23:21:26
-Discription: 
-Environment: 
-'''
-import torch.nn as nn
-import torch.nn.functional as F
-class MLP(nn.Module):
-    
-    ''' 多层感知机
-        输入：state维度
-        输出：概率
-    '''
-    def __init__(self,input_dim,hidden_dim = 36):
-        super(MLP, self).__init__()
-        # 24和36为hidden layer的层数，可根据input_dim, action_dim的情况来改变
-        self.fc1 = nn.Linear(input_dim, hidden_dim)
-        self.fc2 = nn.Linear(hidden_dim,hidden_dim)
-        self.fc3 = nn.Linear(hidden_dim, 1)  # Prob of Left
-
-    def forward(self, x):
-        x = F.relu(self.fc1(x))
-        x = F.relu(self.fc2(x))
-        x = F.sigmoid(self.fc3(x))
-        return x

codes/PolicyGradient/pg.py

@@ -5,21 +5,41 @@ Author: John
 Email: johnjim0816@gmail.com
 Date: 2020-11-22 23:27:44
 LastEditor: John
-LastEditTime: 2021-10-16 00:43:52
+LastEditTime: 2022-02-10 01:25:27
 Discription: 
 Environment: 
 '''
 import torch
+import torch.nn as nn
+import torch.nn.functional as F
 from torch.distributions import Bernoulli
 from torch.autograd import Variable
 import numpy as np
-from PolicyGradient.model import MLP
 
+class MLP(nn.Module):
+    
+    ''' 多层感知机
+        输入：state维度
+        输出：概率
+    '''
+    def __init__(self,input_dim,hidden_dim = 36):
+        super(MLP, self).__init__()
+        # 24和36为hidden layer的层数，可根据input_dim, n_actions的情况来改变
+        self.fc1 = nn.Linear(input_dim, hidden_dim)
+        self.fc2 = nn.Linear(hidden_dim,hidden_dim)
+        self.fc3 = nn.Linear(hidden_dim, 1)  # Prob of Left
+
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = F.sigmoid(self.fc3(x))
+        return x
+        
 class PolicyGradient:
     
-    def __init__(self, state_dim,cfg):
+    def __init__(self, n_states,cfg):
         self.gamma = cfg.gamma
-        self.policy_net = MLP(state_dim,hidden_dim=cfg.hidden_dim)
+        self.policy_net = MLP(n_states,hidden_dim=cfg.hidden_dim)
         self.optimizer = torch.optim.RMSprop(self.policy_net.parameters(), lr=cfg.lr)
         self.batch_size = cfg.batch_size
 

codes/PolicyGradient/task0.py

@@ -0,0 +1,152 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2020-11-22 23:21:53
+LastEditor: John
+LastEditTime: 2022-02-10 06:13:21
+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 gym
+import torch
+import datetime
+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 env_agent_config(cfg,seed=1):
+    env = gym.make(cfg.env_name)  
+    env.seed(seed)
+    n_states = env.observation_space.shape[0]
+    agent = PolicyGradient(n_states,cfg)
+    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
+    action_pool = []
+    reward_pool = [] 
+    rewards = []
+    ma_rewards = []
+    for i_ep in range(cfg.train_eps):
+        state = env.reset()
+        ep_reward = 0
+        for _ in count():
+            action = agent.choose_action(state) # 根据当前环境state选择action
+            next_state, reward, done, _ = env.step(action)
+            ep_reward += reward
+            if done:
+                reward = 0
+            state_pool.append(state)
+            action_pool.append(float(action))
+            reward_pool.append(reward)
+            state = next_state
+            if done:
+                print('回合：{}/{}, 奖励：{}'.format(i_ep + 1, cfg.train_eps, ep_reward))
+                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
+            action_pool = []
+            reward_pool = []
+        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
+            
+
+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}')
+    rewards = []
+    ma_rewards = []
+    for i_ep in range(cfg.test_eps):
+        state = env.reset()
+        ep_reward = 0
+        for _ in count():
+            action = agent.choose_action(state) # 根据当前环境state选择action
+            next_state, reward, done, _ = env.step(action)
+            ep_reward += reward
+            if done:
+                reward = 0
+            state = next_state
+            if done:
+                print('回合：{}/{}, 奖励：{}'.format(i_ep + 1, cfg.train_eps, ep_reward))
+                break
+        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('完成测试！')
+    env.close()
+    return 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
+

codes/PolicyGradient/task0_train.py

@@ -1,136 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-Author: John
-Email: johnjim0816@gmail.com
-Date: 2020-11-22 23:21:53
-LastEditor: John
-LastEditTime: 2021-10-16 00:34:13
-Discription: 
-Environment: 
-'''
-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) # 添加父路径到系统路径sys.path
-
-import gym
-import torch
-import datetime
-from itertools import count
-
-from PolicyGradient.agent import PolicyGradient
-from common.plot import plot_rewards
-from common.utils import save_results,make_dir
-
-curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # 获取当前时间
-
-class PGConfig:
-    def __init__(self):
-        self.algo = "PolicyGradient"  # 算法名称
-        self.env = 'CartPole-v0' # 环境名称
-        self.result_path = curr_path+"/outputs/" + self.env + \
-            '/'+curr_time+'/results/'  # 保存结果的路径
-        self.model_path = curr_path+"/outputs/" + self.env + \
-            '/'+curr_time+'/models/'  # 保存模型的路径
-        self.train_eps = 300 # 训练的回合数
-        self.test_eps = 30 # 测试的回合数
-        self.batch_size = 8
-        self.lr = 0.01 # 学习率
-        self.gamma = 0.99
-        self.hidden_dim = 36 # dimmension of hidden layer
-        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)  
-    env.seed(seed)
-    state_dim = env.observation_space.shape[0]
-    agent = PolicyGradient(state_dim,cfg)
-    return env,agent
-
-def train(cfg,env,agent):
-    print('Start to eval !')
-    print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
-    state_pool = [] # 存放每batch_size个episode的state序列
-    action_pool = []
-    reward_pool = [] 
-    rewards = []
-    ma_rewards = []
-    for i_ep in range(cfg.train_eps):
-        state = env.reset()
-        ep_reward = 0
-        for _ in count():
-            action = agent.choose_action(state) # 根据当前环境state选择action
-            next_state, reward, done, _ = env.step(action)
-            ep_reward += reward
-            if done:
-                reward = 0
-            state_pool.append(state)
-            action_pool.append(float(action))
-            reward_pool.append(reward)
-            state = next_state
-            if done:
-                print('Episode:', i_ep, ' Reward:',  ep_reward)
-                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
-            action_pool = []
-            reward_pool = []
-        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('complete training！')
-    return rewards, ma_rewards
-            
-
-def eval(cfg,env,agent):
-    print('Start to eval !')
-    print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
-    rewards = []
-    ma_rewards = []
-    for i_ep in range(cfg.test_eps):
-        state = env.reset()
-        ep_reward = 0
-        for _ in count():
-            action = agent.choose_action(state) # 根据当前环境state选择action
-            next_state, reward, done, _ = env.step(action)
-            ep_reward += reward
-            if done:
-                reward = 0
-            state = next_state
-            if done:
-                print('Episode:', i_ep, ' Reward:',  ep_reward)
-                break
-        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('complete evaling！')
-    return rewards, ma_rewards
-    
-if __name__ == "__main__":
-    cfg = PGConfig()
-
-     # train
-    env,agent = env_agent_config(cfg,seed=1)
-    rewards, ma_rewards = train(cfg, env, agent)
-    make_dir(cfg.result_path, cfg.model_path)
-    agent.save(path=cfg.model_path)
-    save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
-    plot_rewards(rewards, ma_rewards, tag="train",
-                 algo=cfg.algo, path=cfg.result_path)
-    # eval
-    env,agent = env_agent_config(cfg,seed=10)
-    agent.load(path=cfg.model_path)
-    rewards,ma_rewards = eval(cfg,env,agent)
-    save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
-    plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
-