111 lines
4.5 KiB
Python
111 lines
4.5 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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'''
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Author: John
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Email: johnjim0816@gmail.com
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Date: 2021-03-11 14:26:44
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LastEditor: John
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LastEditTime: 2022-08-15 18:12:13
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Discription:
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Environment:
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'''
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import sys,os
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curr_path = os.path.dirname(os.path.abspath(__file__)) # 当前文件所在绝对路径
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parent_path = os.path.dirname(curr_path) # 父路径
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sys.path.append(parent_path) # 添加路径到系统路径
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import datetime
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import argparse
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from common.utils import save_results,save_args,plot_rewards
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from MonteCarlo.agent import FisrtVisitMC
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from envs.racetrack_env import RacetrackEnv
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curr_time = datetime.datetime.now().strftime(
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"%Y%m%d-%H%M%S") # obtain current time
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def get_args():
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""" 超参数
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"""
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curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
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parser = argparse.ArgumentParser(description="hyperparameters")
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parser.add_argument('--algo_name',default='First-Visit MC',type=str,help="name of algorithm")
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parser.add_argument('--env_name',default='Racetrack',type=str,help="name of environment")
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parser.add_argument('--train_eps',default=200,type=int,help="episodes of training")
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parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing")
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parser.add_argument('--gamma',default=0.9,type=float,help="discounted factor")
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parser.add_argument('--epsilon',default=0.15,type=float,help="the probability to select a random action")
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parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda")
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parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
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'/' + curr_time + '/results/' )
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parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
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'/' + curr_time + '/models/' )
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parser.add_argument('--show_fig',default=False,type=bool,help="if show figure or not")
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parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")
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args = parser.parse_args()
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return args
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def env_agent_config(cfg,seed=1):
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env = RacetrackEnv()
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n_actions = env.action_space.n
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agent = FisrtVisitMC(n_actions, cfg)
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return env,agent
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def train(cfg, env, agent):
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print("开始训练！")
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print(f"环境：{cfg.env_name}，算法：{cfg.algo_name}，设备：{cfg.device}")
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rewards = []
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for i_ep in range(cfg.train_eps):
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state = env.reset()
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ep_reward = 0
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one_ep_transition = []
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while True:
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action = agent.sample(state)
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next_state, reward, done = env.step(action)
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ep_reward += reward
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one_ep_transition.append((state, action, reward))
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state = next_state
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if done:
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break
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rewards.append(ep_reward)
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agent.update(one_ep_transition)
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if (i_ep+1) % 10 == 0:
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print(f"Episode:{i_ep+1}/{cfg.train_eps}: Reward:{ep_reward}")
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print("完成训练")
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return {'rewards':rewards}
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def test(cfg, env, agent):
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print("开始测试！")
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print(f"环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}")
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rewards = []
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for i_ep in range(cfg.test_eps):
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state = env.reset()
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ep_reward = 0
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while True:
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action = agent.predict(state)
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next_state, reward, done = env.step(action)
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ep_reward += reward
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state = next_state
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if done:
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break
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rewards.append(ep_reward)
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print(f'回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.2f}')
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return {'rewards':rewards}
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if __name__ == "__main__":
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cfg = get_args()
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# 训练
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env, agent = env_agent_config(cfg)
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res_dic = train(cfg, env, agent)
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save_args(cfg,path = cfg.result_path) # 保存参数到模型路径上
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agent.save(path = cfg.model_path) # 保存模型
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save_results(res_dic, tag = 'train', path = cfg.result_path)
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plot_rewards(res_dic['rewards'], cfg, path = cfg.result_path,tag = "train")
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# 测试
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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
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agent.load(path = cfg.model_path) # 导入模型
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res_dic = test(cfg, env, agent)
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save_results(res_dic, tag='test',
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path = cfg.result_path) # 保存结果
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plot_rewards(res_dic['rewards'], cfg, path = cfg.result_path,tag = "test") # 画出结果
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