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/MonteCarlo/task0.py
+++ b/projects/codes/MonteCarlo/task0.py
@@ -0,0 +1,110 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2021-03-11 14:26:44
+LastEditor: John
+LastEditTime: 2022-08-15 18:12:13
+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)  # 添加路径到系统路径
+
+import datetime
+import argparse
+from common.utils import save_results,save_args,plot_rewards
+
+from MonteCarlo.agent import FisrtVisitMC
+from envs.racetrack_env import RacetrackEnv
+
+curr_time = datetime.datetime.now().strftime(
+    "%Y%m%d-%H%M%S")  # obtain current time
+
+def get_args():
+    """ 超参数
+    """
+    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='First-Visit MC',type=str,help="name of algorithm")
+    parser.add_argument('--env_name',default='Racetrack',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=0.9,type=float,help="discounted factor")
+    parser.add_argument('--epsilon',default=0.15,type=float,help="the probability to select a random action")
+    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/' ) 
+    parser.add_argument('--show_fig',default=False,type=bool,help="if show figure or not")           
+    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):
+    env = RacetrackEnv()
+    n_actions = env.action_space.n
+    agent = FisrtVisitMC(n_actions, 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}")
+    rewards = []
+    for i_ep in range(cfg.train_eps):
+        state = env.reset()
+        ep_reward = 0
+        one_ep_transition = []
+        while True:
+            action = agent.sample(state)
+            next_state, reward, done = env.step(action)
+            ep_reward += reward
+            one_ep_transition.append((state, action, reward))
+            state = next_state
+            if done:
+                break
+        rewards.append(ep_reward)
+        agent.update(one_ep_transition)
+        if (i_ep+1) % 10 == 0:
+            print(f"Episode:{i_ep+1}/{cfg.train_eps}: Reward:{ep_reward}")
+    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 = []
+    for i_ep in range(cfg.test_eps):
+        state = env.reset()
+        ep_reward = 0
+        while True:
+            action = agent.predict(state)
+            next_state, reward, done = env.step(action)
+            ep_reward += reward
+            state = next_state
+            if done:
+                break
+        rewards.append(ep_reward)
+        print(f'回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.2f}')
+    return {'rewards':rewards}
+    
+if __name__ == "__main__":
+    cfg = get_args()
+     # 训练
+    env, agent = env_agent_config(cfg)
+    res_dic = train(cfg, env, agent)
+    save_args(cfg,path = cfg.result_path) # 保存参数到模型路径上
+    agent.save(path = cfg.model_path)  # 保存模型
+    save_results(res_dic, tag = 'train', path = cfg.result_path)  
+    plot_rewards(res_dic['rewards'], cfg, path = cfg.result_path,tag = "train")  
+    # 测试
+    env, agent = env_agent_config(cfg) # 也可以不加，加这一行的是为了避免训练之后环境可能会出现问题，因此新建一个环境用于测试
+    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, path = cfg.result_path,tag = "test")  # 画出结果