update codes

2021-11-18 15:41:27 +08:00
parent 442e307b01
commit 129c0c65fa
103 changed files with 1025 additions and 558 deletions
--- a/codes/DQN/task0_train.py
+++ b/codes/DQN/task0_train.py
@@ -0,0 +1,137 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-12 00:48:57
+@LastEditor: John
+LastEditTime: 2021-09-15 15:34:13
+@Discription: 
+@Environment: python 3.7.7
+'''
+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
+
+import gym
+import torch
+import datetime
+
+from common.utils import save_results, make_dir
+from common.plot import plot_rewards
+from DQN.agent import DQN
+
+curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # 获取当前时间
+class DQNConfig:
+    def __init__(self):
+        self.algo = "DQN"  # 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.eval_eps = 30 # 测试的回合数
+        # 超参数
+        self.gamma = 0.95 # 强化学习中的折扣因子
+        self.epsilon_start = 0.90 # 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 = 4 # 目标网络的更新频率
+        self.hidden_dim = 256  # 网络隐藏层
+class PlotConfig:
+    def __init__(self) -> None:
+        self.algo = "DQN"  # 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.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):
+    ''' 创建环境和智能体
+    '''
+    env = gym.make(cfg.env_name)  # 创建环境
+    env.seed(seed) # 设置随机种子
+    n_states = env.observation_space.shape[0] # 状态数
+    n_actions = env.action_space.n # 动作数
+    agent = DQN(n_states,n_actions,cfg) # 创建智能体
+    return env,agent
+    
+def train(cfg, env, agent):
+    ''' 训练
+    '''
+    print('开始训练!')
+    print(f'环境：{cfg.env_name}, 算法：{cfg.algo}, 设备：{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):
+        ep_reward = 0 # 记录一回合内的奖励
+        state = env.reset() # 重置环境，返回初始状态
+        while True:
+            action = agent.choose_action(state) # 选择动作
+            next_state, reward, done, _ = env.step(action) # 更新环境，返回transition
+            agent.memory.push(state, action, reward, next_state, done) # 保存transition
+            state = next_state # 更新下一个状态
+            agent.update() # 更新智能体
+            ep_reward += reward # 累加奖励
+            if done:
+                break
+        if (i_ep+1) % cfg.target_update == 0: # 智能体目标网络更新
+            agent.target_net.load_state_dict(agent.policy_net.state_dict())
+        if (i_ep+1)%10 == 0: 
+            print('回合：{}/{}, 奖励：{}'.format(i_ep+1, cfg.train_eps, ep_reward))
+        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！')
+    return rewards, ma_rewards
+
+def eval(cfg,env,agent):
+    print('开始测试!')
+    print(f'环境：{cfg.env_name}, 算法：{cfg.algo}, 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
+    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.eval_eps):
+        ep_reward = 0 # 记录一回合内的奖励
+        state = env.reset() # 重置环境，返回初始状态
+        while True:
+            action = agent.choose_action(state) # 选择动作
+            next_state, reward, done, _ = env.step(action) # 更新环境，返回transition
+            state = next_state # 更新下一个状态
+            ep_reward += reward # 累加奖励
+            if done:
+                break
+        rewards.append(ep_reward)
+        if ma_rewards:
+            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.eval_eps}, 奖励：{ep_reward:.1f}")
+    print('完成测试！')
+    return rewards,ma_rewards
+
+if __name__ == "__main__":
+    cfg = DQNConfig()
+    plot_cfg = PlotConfig()
+    # 训练
+    env,agent = env_agent_config(cfg,seed=1)
+    rewards, ma_rewards = train(cfg, env, agent)
+    make_dir(plot_cfg.result_path, plot_cfg.model_path) # 创建保存结果和模型路径的文件夹
+    agent.save(path=plot_cfg.model_path) # 保存模型
+    save_results(rewards, ma_rewards, tag='train', path=plot_cfg.result_path) # 保存结果
+    plot_rewards(rewards, ma_rewards, plot_cfg, tag="train") # 画出结果
+    # 测试
+    env,agent = env_agent_config(cfg,seed=10)
+    agent.load(path=plot_cfg.model_path) # 导入模型
+    rewards,ma_rewards = eval(cfg,env,agent)
+    save_results(rewards,ma_rewards,tag='eval',path=plot_cfg.result_path) # 保存结果
+    plot_rewards(rewards,ma_rewards, plot_cfg, tag="eval") # 画出结果