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johnjim0816
2021-09-15 10:32:52 +08:00
parent 95f3f4dd57
commit 5085040330
74 changed files with 431 additions and 433 deletions
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codes/QLearning/task0_train.py
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@@ -5,14 +5,14 @@ Author: John
Email: johnjim0816@gmail.com
Date: 2020-09-11 23:03:00
LastEditor: John
LastEditTime: 2021-05-06 17:04:38
LastEditTime: 2021-09-12 01:29:40
Discription:
Environment:
'''
import sys,os
curr_path = os.path.dirname(__file__)
parent_path=os.path.dirname(curr_path)
sys.path.append(parent_path) # add current terminal path to sys.path
curr_path = os.path.dirname(os.path.abspath(__file__)) # 当前路径
parent_path=os.path.dirname(curr_path) # 父路径，这里就是我们的项目路径
sys.path.append(parent_path) # 由于需要引用项目路径下的其他模块比如envs，所以需要添加路径到sys.path
import gym
import torch
@@ -20,49 +20,49 @@ import datetime
from envs.gridworld_env import CliffWalkingWapper
from QLearning.agent import QLearning
from common.plot import plot_rewards
from common.plot import plot_rewards,plot_rewards_cn
from common.utils import save_results,make_dir
curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
class QlearningConfig:
'''训练相关参数'''
def __init__(self):
self.algo = 'Qlearning'
self.env = 'CliffWalking-v0' # 0 up, 1 right, 2 down, 3 left
self.result_path = curr_path+"/outputs/" +self.env+'/'+curr_time+'/results/' # path to save results
self.model_path = curr_path+"/outputs/" +self.env+'/'+curr_time+'/models/' # path to save models
self.train_eps = 300 # 训练的episode数目
self.eval_eps = 30
self.algo = 'Q-learning' # 算法名称
self.env = 'CliffWalking-v0' # 环境名称
self.result_path = curr_path+"/outputs/" +self.env+'/'+curr_time+'/results/' # 保存结果的路径
self.model_path = curr_path+"/outputs/" +self.env+'/'+curr_time+'/models/' # 保存模型的路径
self.train_eps = 200 # 训练的回合数
self.eval_eps = 30 # 测试的回合数
self.gamma = 0.9 # reward的衰减率
self.epsilon_start = 0.95 # e-greedy策略中初始epsilon
self.epsilon_start = 0.90 # 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 # learning rate
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # check gpu
self.lr = 0.05 # 学习率
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测GPU
def env_agent_config(cfg,seed=1):
env = gym.make(cfg.env)
env = CliffWalkingWapper(env)
env.seed(seed)
state_dim = env.observation_space.n
action_dim = env.action_space.n
agent = QLearning(state_dim,action_dim,cfg)
env.seed(seed) # 设置随机种子
n_states = env.observation_space.n # 状态维度
n_actions = env.action_space.n # 动作维度
agent = QLearning(n_states,n_actions,cfg)
return env,agent
def train(cfg,env,agent):
print('Start to train !')
print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
print('开始训练！')
print(f'环境:{cfg.env}, 算法:{cfg.algo}, 设备:{cfg.device}')
rewards = []
ma_rewards = [] # moving average reward
ma_rewards = [] # 滑动平均奖励
for i_ep in range(cfg.train_eps):
ep_reward = 0 # 记录每个episode的reward
ep_reward = 0 # 记录每个回合的奖励
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
state = next_state # 更新状态
ep_reward += reward
if done:
break
@@ -71,20 +71,18 @@ def train(cfg,env,agent):
ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
else:
ma_rewards.append(ep_reward)
print("Episode:{}/{}: reward:{:.1f}".format(i_ep+1, cfg.train_eps,ep_reward))
print('Complete training！')
print("回合数：{}/{}，奖励{:.1f}".format(i_ep+1, cfg.train_eps,ep_reward))
print('完成训练！')
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)
print('Start to eval !')
print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
rewards = [] # 记录所有episode的reward
ma_rewards = [] # 滑动平均的reward
print('开始测试！')
print(f'环境：{cfg.env}, 算法：{cfg.algo}, 设备：{cfg.device}')
rewards = [] # 记录所有回合的奖励
ma_rewards = [] # 滑动平均的奖励
for i_ep in range(cfg.eval_eps):
ep_reward = 0 # 记录每个episode的reward
state = env.reset() # 重置环境, 重新开一局（即开始新的一个episode）
state = env.reset() # 重置环境, 重新开一局（即开始新的一个回合）
while True:
action = agent.predict(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
@@ -97,23 +95,26 @@ def eval(cfg,env,agent):
ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
else:
ma_rewards.append(ep_reward)
print(f"Episode:{i_ep+1}/{cfg.eval_eps}, reward:{ep_reward:.1f}")
print('Complete evaling！')
print(f"回合数：{i_ep+1}/{cfg.eval_eps}, 奖励：{ep_reward:.1f}")
print('完成测试！')
return rewards,ma_rewards
if __name__ == "__main__":
cfg = QlearningConfig()
# 训练
env,agent = env_agent_config(cfg,seed=1)
rewards,ma_rewards = train(cfg,env,agent)
make_dir(cfg.result_path,cfg.model_path)
agent.save(path=cfg.model_path)
save_results(rewards,ma_rewards,tag='train',path=cfg.result_path)
plot_rewards(rewards,ma_rewards,tag="train",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
make_dir(cfg.result_path,cfg.model_path) # 创建文件夹
agent.save(path=cfg.model_path) # 保存模型
save_results(rewards,ma_rewards,tag='train',path=cfg.result_path) # 保存结果
plot_rewards_cn(rewards,ma_rewards,tag="train",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
env,agent = env_agent_config(cfg,seed=10)
agent.load(path=cfg.model_path)
# # 测试
# env,agent = env_agent_config(cfg,seed=10)
# agent.load(path=cfg.model_path) # 加载模型
rewards,ma_rewards = eval(cfg,env,agent)
save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
plot_rewards_cn(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)