114 lines
4.8 KiB
Python
114 lines
4.8 KiB
Python
#!/usr/bin/env python
|
||
# coding=utf-8
|
||
'''
|
||
Author: John
|
||
Email: johnjim0816@gmail.com
|
||
Date: 2020-09-11 23:03:00
|
||
LastEditor: John
|
||
LastEditTime: 2021-03-12 21:16:50
|
||
Discription:
|
||
Environment:
|
||
'''
|
||
|
||
import sys,os
|
||
sys.path.append(os.getcwd()) # 添加当前终端路径
|
||
import gym
|
||
import datetime
|
||
|
||
from envs.gridworld_env import CliffWalkingWapper, FrozenLakeWapper
|
||
from QLearning.agent import QLearning
|
||
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 QlearningConfig:
|
||
'''训练相关参数'''
|
||
def __init__(self):
|
||
self.n_episodes = 200 # 训练的episode数目
|
||
self.gamma = 0.9 # reward的衰减率
|
||
self.epsilon_start = 0.99 # e-greedy策略中初始epsilon
|
||
self.epsilon_end = 0.01 # e-greedy策略中的终止epsilon
|
||
self.epsilon_decay = 200 # e-greedy策略中epsilon的衰减率
|
||
self.lr = 0.1 # 学习率
|
||
|
||
def train(cfg,env,agent):
|
||
# env = gym.make("FrozenLake-v0", is_slippery=False) # 0 left, 1 down, 2 right, 3 up
|
||
# env = FrozenLakeWapper(env)
|
||
rewards = [] # 记录所有episode的reward
|
||
ma_rewards = [] # 滑动平均的reward
|
||
steps = [] # 记录所有episode的steps
|
||
for i_episode in range(cfg.n_episodes):
|
||
ep_reward = 0 # 记录每个episode的reward
|
||
ep_steps = 0 # 记录每个episode走了多少step
|
||
state = env.reset() # 重置环境, 重新开一局（即开始新的一个episode）
|
||
while True:
|
||
action = agent.choose_action(state) # 根据算法选择一个动作
|
||
next_state, reward, done, _ = env.step(action) # 与环境进行一次动作交互
|
||
agent.update(state, action, reward, next_state, done) # Q-learning算法更新
|
||
state = next_state # 存储上一个观察值
|
||
ep_reward += reward
|
||
ep_steps += 1 # 计算step数
|
||
if done:
|
||
break
|
||
steps.append(ep_steps)
|
||
rewards.append(ep_reward)
|
||
# 计算滑动平均的reward
|
||
if ma_rewards:
|
||
ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
|
||
else:
|
||
ma_rewards.append(ep_reward)
|
||
print("Episode:{}/{}: reward:{:.1f}".format(i_episode+1, cfg.n_episodes,ep_reward))
|
||
return rewards,ma_rewards
|
||
|
||
def eval(cfg,env,agent):
|
||
# env = gym.make("FrozenLake-v0", is_slippery=False) # 0 left, 1 down, 2 right, 3 up
|
||
# env = FrozenLakeWapper(env)
|
||
rewards = [] # 记录所有episode的reward
|
||
ma_rewards = [] # 滑动平均的reward
|
||
steps = [] # 记录所有episode的steps
|
||
for i_episode in range(cfg.n_episodes):
|
||
ep_reward = 0 # 记录每个episode的reward
|
||
ep_steps = 0 # 记录每个episode走了多少step
|
||
state = env.reset() # 重置环境, 重新开一局（即开始新的一个episode）
|
||
while True:
|
||
action = agent.choose_action(state) # 根据算法选择一个动作
|
||
next_state, reward, done, _ = env.step(action) # 与环境进行一个交互
|
||
state = next_state # 存储上一个观察值
|
||
ep_reward += reward
|
||
ep_steps += 1 # 计算step数
|
||
if done:
|
||
break
|
||
steps.append(ep_steps)
|
||
rewards.append(ep_reward)
|
||
# 计算滑动平均的reward
|
||
if ma_rewards:
|
||
ma_rewards.append(rewards[-1]*0.9+ep_reward*0.1)
|
||
else:
|
||
ma_rewards.append(ep_reward)
|
||
print("Episode:{}/{}: reward:{:.1f}".format(i_episode+1, cfg.n_episodes,ep_reward))
|
||
return rewards,ma_rewards
|
||
|
||
if __name__ == "__main__":
|
||
cfg = QlearningConfig()
|
||
env = gym.make("CliffWalking-v0") # 0 up, 1 right, 2 down, 3 left
|
||
env = CliffWalkingWapper(env)
|
||
n_actions = env.action_space.n
|
||
agent = QLearning(n_actions,cfg)
|
||
rewards,ma_rewards = train(cfg,env,agent)
|
||
agent.save(path=SAVED_MODEL_PATH)
|
||
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)
|
||
|
||
|