120 lines
4.1 KiB
Python
120 lines
4.1 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: 2022-04-24 22:17:05
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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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import datetime
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import torch
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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.utils import save_results,make_dir,plot_rewards
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curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
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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
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self.test_eps = 20
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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.n_steps = 200
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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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action_dim = 9
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agent = Sarsa(action_dim,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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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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ep_reward = 0
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# while True:
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for _ in range(cfg.n_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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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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def eval(cfg,env,agent):
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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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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 evaling！')
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return rewards,ma_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 = eval(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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