119 lines
3.9 KiB
Python
119 lines
3.9 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: 2021-05-05 17:27:50
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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(__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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import torch
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import datetime
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from common.utils import save_results,make_dir
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from common.plot import 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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class MCConfig:
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def __init__(self):
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self.algo = "MC" # name of algo
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self.env = 'Racetrack'
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self.result_path = curr_path+"/outputs/" + self.env + \
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'/'+curr_time+'/results/' # path to save results
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self.model_path = curr_path+"/outputs/" + self.env + \
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'/'+curr_time+'/models/' # path to save models
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# epsilon: The probability to select a random action .
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self.epsilon = 0.15
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self.gamma = 0.9 # gamma: Gamma discount factor.
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self.train_eps = 200
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self.device = torch.device(
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"cuda" if torch.cuda.is_available() else "cpu") # check gpu
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def env_agent_config(cfg,seed=1):
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env = RacetrackEnv()
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n_actions = 9
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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('Start to eval !')
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print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
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rewards = []
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ma_rewards = [] # moving average 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.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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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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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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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('Complete training！')
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return rewards, ma_rewards
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def eval(cfg, env, agent):
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print('Start to eval !')
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print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
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rewards = []
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ma_rewards = [] # moving average 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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while True:
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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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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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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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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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return rewards, ma_rewards
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if __name__ == "__main__":
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cfg = MCConfig()
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# train
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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, tag="train",
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algo=cfg.algo, path=cfg.result_path)
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# eval
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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 = eval(cfg,env,agent)
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save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
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plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
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