81 lines
3.0 KiB
Python
81 lines
3.0 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 17:59:16
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LastEditor: John
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LastEditTime: 2021-03-12 17:01:43
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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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sys.path.append(os.getcwd())
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import datetime
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from envs.racetrack_env import RacetrackEnv
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from Sarsa.agent import Sarsa
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from common.plot import plot_rewards
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from common.utils import save_results
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SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
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SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/' # 生成保存的模型路径
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if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"): # 检测是否存在文件夹
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os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/")
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if not os.path.exists(SAVED_MODEL_PATH): # 检测是否存在文件夹
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os.mkdir(SAVED_MODEL_PATH)
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RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/results/"+SEQUENCE+'/' # 存储reward的路径
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if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/results/"): # 检测是否存在文件夹
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os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/results/")
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if not os.path.exists(RESULT_PATH): # 检测是否存在文件夹
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os.mkdir(RESULT_PATH)
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class SarsaConfig:
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''' parameters for Sarsa
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'''
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def __init__(self):
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self.epsilon = 0.15 # epsilon: The probability to select a random action .
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self.gamma = 0.9 # gamma: Gamma discount factor.
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self.lr = 0.2 # learning rate: step size parameter
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self.n_episodes = 150
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self.n_steps = 2000
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def sarsa_train(cfg,env,agent):
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rewards = []
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ma_rewards = []
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for i_episode in range(cfg.n_episodes):
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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 t in range(cfg.n_steps):
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action = agent.choose_action(state)
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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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agent.update(state, action, reward, next_state, next_action,done)
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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_episode+1)%10==0:
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# print("Episode:{}/{}: Reward:{}".format(i_episode+1, cfg.n_episodes,ep_reward))
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return rewards,ma_rewards
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if __name__ == "__main__":
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sarsa_cfg = SarsaConfig()
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env = RacetrackEnv()
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action_dim=9
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agent = Sarsa(action_dim,sarsa_cfg)
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rewards,ma_rewards = sarsa_train(sarsa_cfg,env,agent)
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agent.save(path=SAVED_MODEL_PATH)
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save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)
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plot_rewards(rewards,ma_rewards,tag="train",algo = "On-Policy First-Visit MC Control",path=RESULT_PATH)
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