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johnjim0816
@@ -5,115 +5,114 @@ Author: John
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Email: johnjim0816@gmail.com
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Date: 2021-03-11 17:59:16
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LastEditor: John
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LastEditTime: 2022-04-29 20:18:13
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LastEditTime: 2022-08-04 22:28:51
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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__)) # current path of file
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parent_path = os.path.dirname(curr_path)
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sys.path.append(parent_path) # add current terminal path to sys.path
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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 torch
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import argparse
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from envs.racetrack_env import RacetrackEnv
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from Sarsa.sarsa import Sarsa
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from common.utils import save_results,make_dir,plot_rewards
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from common.utils import save_results,make_dir,plot_rewards,save_args
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curr_time = datetime.datetime.now().strftime("%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='Sarsa',type=str,help="name of algorithm")
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parser.add_argument('--env_name',default='CliffWalking-v0',type=str,help="name of environment")
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parser.add_argument('--train_eps',default=300,type=int,help="episodes of training") # 训练的回合数
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parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing") # 测试的回合数
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parser.add_argument('--ep_max_steps',default=200,type=int) # 每回合最大的部署
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parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor") # 折扣因子
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parser.add_argument('--epsilon_start',default=0.90,type=float,help="initial value of epsilon") # e-greedy策略中初始epsilon
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parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon") # e-greedy策略中的终止epsilon
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parser.add_argument('--epsilon_decay',default=200,type=int,help="decay rate of epsilon") # e-greedy策略中epsilon的衰减率
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parser.add_argument('--lr',default=0.2,type=float,help="learning rate")
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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/' ) # path to save models
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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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class Config:
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''' parameters for Sarsa
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'''
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def __init__(self):
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self.algo_name = 'Qlearning'
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self.env_name = 'CliffWalking-v0' # 0 up, 1 right, 2 down, 3 left
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # check GPU
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self.result_path = curr_path+"/outputs/" +self.env_name+'/'+curr_time+'/results/' # path to save results
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self.model_path = curr_path+"/outputs/" +self.env_name+'/'+curr_time+'/models/' # path to save models
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self.train_eps = 300 # training episodes
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self.test_eps = 20 # testing episodes
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self.n_steps = 200 # maximum steps per episode
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self.epsilon_start = 0.90 # start value of epsilon
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self.epsilon_end = 0.01 # end value of epsilon
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self.epsilon_decay = 200 # decay rate of epsilon
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self.gamma = 0.99 # gamma: Gamma discount factor.
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self.lr = 0.2 # learning rate: step size parameter
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self.save = True # if save figures
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def env_agent_config(cfg,seed=1):
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env = RacetrackEnv()
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n_states = 9 # number of actions
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agent = Sarsa(n_states,cfg)
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n_actions = 9 # 动作数
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agent = Sarsa(n_actions,cfg)
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return env,agent
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def train(cfg,env,agent):
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rewards = []
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ma_rewards = []
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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 = [] # 记录奖励
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for i_ep in range(cfg.train_eps):
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state = env.reset()
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action = agent.choose_action(state)
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action = agent.sample(state)
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ep_reward = 0
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# while True:
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for _ in range(cfg.n_steps):
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for _ in range(cfg.ep_max_steps):
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next_state, reward, done = env.step(action)
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ep_reward+=reward
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next_action = agent.choose_action(next_state)
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next_action = agent.sample(next_state)
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agent.update(state, action, reward, next_state, next_action,done)
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state = next_state
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action = next_action
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if done:
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break
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if ma_rewards:
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ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
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else:
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ma_rewards.append(ep_reward)
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rewards.append(ep_reward)
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if (i_ep+1)%2==0:
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print(f"Episode:{i_ep+1}, Reward:{ep_reward}, Epsilon:{agent.epsilon}")
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return rewards,ma_rewards
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print(f"回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.1f}，Epsilon：{agent.epsilon}")
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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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ma_rewards = []
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for i_ep in range(cfg.test_eps):
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# Print out which episode we're on, useful for debugging.
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# Generate an episode.
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# An episode is an array of (state, action, reward) tuples
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state = env.reset()
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ep_reward = 0
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while True:
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# for _ in range(cfg.n_steps):
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action = agent.predict_action(state)
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# while True:
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for _ in range(cfg.ep_max_steps):
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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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if ma_rewards:
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ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
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else:
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ma_rewards.append(ep_reward)
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rewards.append(ep_reward)
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if (i_ep+1)%1==0:
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print("Episode:{}/{}: Reward:{}".format(i_ep+1, cfg.test_eps,ep_reward))
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print('Complete testing！')
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return rewards,ma_rewards
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print(f"回合数：{i_ep+1}/{cfg.test_eps}, 奖励：{ep_reward:.1f}")
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print('完成测试！')
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return {"rewards":rewards}
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if __name__ == "__main__":
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cfg = Config()
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env,agent = env_agent_config(cfg,seed=1)
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rewards,ma_rewards = train(cfg,env,agent)
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make_dir(cfg.result_path,cfg.model_path)
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agent.save(path=cfg.model_path)
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save_results(rewards,ma_rewards,tag='train',path=cfg.result_path)
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plot_rewards(rewards, ma_rewards, cfg, tag="train")
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env,agent = env_agent_config(cfg,seed=10)
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agent.load(path=cfg.model_path)
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rewards,ma_rewards = test(cfg,env,agent)
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save_results(rewards,ma_rewards,tag='test',path=cfg.result_path)
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plot_rewards(rewards, ma_rewards, cfg, tag="test")
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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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make_dir(cfg.result_path, cfg.model_path)
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save_args(cfg) # save parameters
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agent.save(path=cfg.model_path) # save model
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save_results(res_dic, tag='train',
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path=cfg.result_path)
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plot_rewards(res_dic['rewards'], cfg, tag="train")
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# 测试
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env, agent = env_agent_config(cfg)
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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, tag="test") # 画出结果
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