update projects

2022-08-15 22:31:37 +08:00
parent cd27cb67b7
commit 73948f1dc8
109 changed files with 3483 additions and 1011 deletions
--- a/projects/codes/Sarsa/task1.py
+++ b/projects/codes/Sarsa/task1.py
@@ -0,0 +1,131 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2020-09-11 23:03:00
+LastEditor: John
+LastEditTime: 2022-08-04 22:44:00
+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 gym
+import torch
+import datetime
+import argparse
+from envs.gridworld_env import CliffWalkingWapper
+from Sarsa.sarsa import Sarsa
+from common.utils import plot_rewards,save_args
+from common.utils import save_results,make_dir
+
+
+def get_args():
+    """ 
+    """
+    curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # 获取当前时间
+    parser = argparse.ArgumentParser(description="hyperparameters")      
+    parser.add_argument('--algo_name',default='Sarsa',type=str,help="name of algorithm")
+    parser.add_argument('--env_name',default='CliffWalking-v0',type=str,help="name of environment")
+    parser.add_argument('--train_eps',default=400,type=int,help="episodes of training") # 训练的回合数
+    parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing") # 测试的回合数
+    parser.add_argument('--gamma',default=0.90,type=float,help="discounted factor") # 折扣因子
+    parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon") #  e-greedy策略中初始epsilon
+    parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon") # e-greedy策略中的终止epsilon
+    parser.add_argument('--epsilon_decay',default=300,type=int,help="decay rate of epsilon") # e-greedy策略中epsilon的衰减率
+    parser.add_argument('--lr',default=0.1,type=float,help="learning rate")
+    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
+curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
+      
+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}')
+    rewards = []  # 记录奖励
+    for i_ep in range(cfg.train_eps):
+        ep_reward = 0  # 记录每个回合的奖励
+        state = env.reset()  # 重置环境,即开始新的回合
+        action = agent.sample(state)
+        while True:
+            action = agent.sample(state)  # 根据算法采样一个动作
+            next_state, reward, done, _ = env.step(action)  # 与环境进行一次动作交互
+            next_action = agent.sample(next_state)
+            agent.update(state, action, reward, next_state, next_action,done) # 算法更新
+            state = next_state # 更新状态
+            action = next_action
+            ep_reward += reward
+            if done:
+                break
+        rewards.append(ep_reward)
+        print(f"回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.1f}，Epsilon：{agent.epsilon}")
+    print('完成训练！')
+    return {"rewards":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 = []  # 记录所有回合的奖励
+    for i_ep in range(cfg.test_eps):
+        ep_reward = 0  # 记录每个episode的reward
+        state = env.reset()  # 重置环境, 重新开一局（即开始新的一个回合）
+        while True:
+            action = agent.predict(state)  # 根据算法选择一个动作
+            next_state, reward, done, _ = env.step(action)  # 与环境进行一个交互
+            state = next_state  # 更新状态
+            ep_reward += reward
+            if done:
+                break
+        rewards.append(ep_reward)
+        print(f"回合数：{i_ep+1}/{cfg.test_eps}, 奖励：{ep_reward:.1f}")
+    print('完成测试！')
+    return {"rewards":rewards}
+        
+def env_agent_config(cfg,seed=1):
+    '''创建环境和智能体
+    Args:
+        cfg ([type]): [description]
+        seed (int, optional): 随机种子. Defaults to 1.
+    Returns:
+        env [type]: 环境
+        agent : 智能体
+    '''    
+    env = gym.make(cfg.env_name)  
+    env = CliffWalkingWapper(env)
+    env.seed(seed) # 设置随机种子
+    n_states = env.observation_space.n # 状态维度
+    n_actions = env.action_space.n # 动作维度
+    print(f"状态数：{n_states}，动作数：{n_actions}")
+    agent = Sarsa(n_actions,cfg)
+    return env,agent
+if __name__ == "__main__":
+    cfg = get_args()
+    # 训练
+    env, agent = env_agent_config(cfg)
+    res_dic = train(cfg, env, agent)
+    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'], 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(res_dic, tag='test',
+                 path=cfg.result_path)  # 保存结果
+    plot_rewards(res_dic['rewards'], cfg, tag="test")  # 画出结果
+
+        
+