89 lines
3.3 KiB
Python
89 lines
3.3 KiB
Python
#!/usr/bin/env python
|
|
# coding=utf-8
|
|
'''
|
|
Author: John
|
|
Email: johnjim0816@gmail.com
|
|
Date: 2021-03-11 14:26:44
|
|
LastEditor: John
|
|
LastEditTime: 2021-03-17 12:35:36
|
|
Discription:
|
|
Environment:
|
|
'''
|
|
import sys,os
|
|
sys.path.append(os.getcwd())
|
|
import argparse
|
|
import datetime
|
|
|
|
from envs.racetrack_env import RacetrackEnv
|
|
from MonteCarlo.agent import FisrtVisitMC
|
|
from common.plot import plot_rewards
|
|
from common.utils import save_results
|
|
|
|
SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
|
|
SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/' # 生成保存的模型路径
|
|
if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"): # 检测是否存在文件夹
|
|
os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/")
|
|
if not os.path.exists(SAVED_MODEL_PATH): # 检测是否存在文件夹
|
|
os.mkdir(SAVED_MODEL_PATH)
|
|
RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/results/"+SEQUENCE+'/' # 存储reward的路径
|
|
if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/results/"): # 检测是否存在文件夹
|
|
os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/results/")
|
|
if not os.path.exists(RESULT_PATH): # 检测是否存在文件夹
|
|
os.mkdir(RESULT_PATH)
|
|
|
|
class MCConfig:
|
|
def __init__(self):
|
|
self.epsilon = 0.15 # epsilon: The probability to select a random action .
|
|
self.gamma = 0.9 # gamma: Gamma discount factor.
|
|
self.n_episodes = 150
|
|
self.n_steps = 2000
|
|
|
|
def get_mc_args():
|
|
'''set parameters
|
|
'''
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--epsilon", default=0.15, type=float) # epsilon: The probability to select a random action . float between 0 and 1.
|
|
parser.add_argument("--gamma", default=0.9, type=float) # gamma: Gamma discount factor.
|
|
parser.add_argument("--n_episodes", default=150, type=int)
|
|
parser.add_argument("--n_steps", default=2000, type=int)
|
|
mc_cfg = parser.parse_args()
|
|
return mc_cfg
|
|
|
|
|
|
|
|
def mc_train(cfg,env,agent):
|
|
rewards = []
|
|
ma_rewards = [] # moving average rewards
|
|
for i_episode in range(cfg.n_episodes):
|
|
one_ep_transition = []
|
|
state = env.reset()
|
|
ep_reward = 0
|
|
while True:
|
|
# for t in range(cfg.n_steps):
|
|
action = agent.choose_action(state)
|
|
next_state, reward, done = env.step(action)
|
|
ep_reward+=reward
|
|
one_ep_transition.append((state, action, reward))
|
|
state = next_state
|
|
if done:
|
|
break
|
|
rewards.append(ep_reward)
|
|
if ma_rewards:
|
|
ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
|
|
else:
|
|
ma_rewards.append(ep_reward)
|
|
agent.update(one_ep_transition)
|
|
if (i_episode+1)%10==0:
|
|
print("Episode:{}/{}: Reward:{}".format(i_episode+1, mc_cfg.n_episodes,ep_reward))
|
|
return rewards,ma_rewards
|
|
if __name__ == "__main__":
|
|
mc_cfg = MCConfig()
|
|
env = RacetrackEnv()
|
|
action_dim=9
|
|
agent = FisrtVisitMC(action_dim,mc_cfg)
|
|
rewards,ma_rewards= mc_train(mc_cfg,env,agent)
|
|
save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)
|
|
plot_rewards(rewards,ma_rewards,tag="train",algo = "On-Policy First-Visit MC Control",path=RESULT_PATH)
|
|
|
|
|