update codes

codes/DoubleDQN/task0.py

@@ -0,0 +1,83 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: JiangJi
+Email: johnjim0816@gmail.com
+Date: 2021-11-07 18:10:37
+LastEditor: JiangJi
+LastEditTime: 2021-11-19 18:34:05
+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
+
+import gym
+import torch
+import datetime
+
+from common.utils import save_results, make_dir
+from common.utils import plot_rewards
+from DoubleDQN.agent import DoubleDQN
+from DoubleDQN.train import train,test
+
+curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # 获取当前时间
+algo_name = 'DoubleDQN'  # 算法名称
+env_name = 'CartPole-v0'  # 环境名称
+class DoubleDQNConfig:
+    def __init__(self):
+        self.algo_name = algo_name # 算法名称
+        self.env_name = env_name  # 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
+        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
+class PlotConfig:
+    ''' 绘图相关参数设置
+    '''
+
+    def __init__(self) -> None:
+        self.algo_name = algo_name  # 算法名称
+        self.env_name = env_name  # 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):
+    env = gym.make(cfg.env_name)  
+    env.seed(seed)
+    n_states = env.observation_space.shape[0]
+    n_actions = env.action_space.n
+    agent = DoubleDQN(n_states,n_actions,cfg)
+    return env,agent
+
+cfg = DoubleDQNConfig()
+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 = test(cfg,env,agent)
+save_results(rewards, ma_rewards, tag='test', path=plot_cfg.result_path)  # 保存结果
+plot_rewards(rewards, ma_rewards, plot_cfg, tag="test")  # 画出结果