149 lines
6.1 KiB
Python
149 lines
6.1 KiB
Python
#!/usr/bin/env python
|
|
# coding=utf-8
|
|
'''
|
|
Author: JiangJi
|
|
Email: johnjim0816@gmail.com
|
|
Date: 2021-12-22 11:14:17
|
|
LastEditor: JiangJi
|
|
LastEditTime: 2021-12-22 11:40:44
|
|
Discription: 使用 Nature DQN 训练 CartPole-v1
|
|
'''
|
|
import sys
|
|
import os
|
|
curr_path = os.path.dirname(os.path.abspath(__file__)) # 当前文件所在绝对路径
|
|
parent_path = os.path.dirname(curr_path) # 父路径
|
|
sys.path.append(parent_path) # 添加路径到系统路径
|
|
|
|
import gym
|
|
import torch
|
|
import datetime
|
|
from common.utils import save_results, make_dir
|
|
from common.utils import plot_rewards, plot_rewards_cn
|
|
from DQN.dqn import DQN
|
|
|
|
curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
|
|
algo_name = "DQN" # 算法名称
|
|
env_name = 'CartPole-v1' # 环境名称
|
|
class DQNConfig:
|
|
''' 算法相关参数设置
|
|
'''
|
|
|
|
def __init__(self):
|
|
self.algo_name = algo_name # 算法名称
|
|
self.env_name = env_name # 环境名称
|
|
self.device = torch.device(
|
|
"cuda" if torch.cuda.is_available() else "cpu") # 检测GPU
|
|
self.train_eps = 200 # 训练的回合数
|
|
self.test_eps = 30 # 测试的回合数
|
|
# 超参数
|
|
self.gamma = 0.95 # 强化学习中的折扣因子
|
|
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 = 500 # e-greedy策略中epsilon的衰减率
|
|
self.lr = 0.0001 # 学习率
|
|
self.memory_capacity = 100000 # 经验回放的容量
|
|
self.batch_size = 64 # mini-batch SGD中的批量大小
|
|
self.target_update = 4 # 目标网络的更新频率
|
|
self.hidden_dim = 256 # 网络隐藏层
|
|
class PlotConfig:
|
|
''' 绘图相关参数设置
|
|
'''
|
|
|
|
def __init__(self) -> None:
|
|
self.algo_name = algo_name # 算法名称
|
|
self.env_name = env_name # 环境名称
|
|
self.device = torch.device(
|
|
"cuda" if torch.cuda.is_available() else "cpu") # 检测GPU
|
|
self.result_path = curr_path + "/outputs/" + self.env_name + \
|
|
'/' + curr_time + '/results/' # 保存结果的路径
|
|
self.model_path = curr_path + "/outputs/" + self.env_name + \
|
|
'/' + curr_time + '/models/' # 保存模型的路径
|
|
self.save = True # 是否保存图片
|
|
|
|
|
|
def env_agent_config(cfg, seed=1):
|
|
''' 创建环境和智能体
|
|
'''
|
|
env = gym.make(cfg.env_name) # 创建环境
|
|
env.seed(seed) # 设置随机种子
|
|
n_states = env.observation_space.shape[0] # 状态数
|
|
n_actions = env.action_space.n # 动作数
|
|
agent = DQN(n_states, n_actions, cfg) # 创建智能体
|
|
return env, agent
|
|
|
|
def train(cfg, env, agent):
|
|
''' 训练
|
|
'''
|
|
print('开始训练!')
|
|
print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')
|
|
rewards = [] # 记录所有回合的奖励
|
|
ma_rewards = [] # 记录所有回合的滑动平均奖励
|
|
for i_ep in range(cfg.train_eps):
|
|
ep_reward = 0 # 记录一回合内的奖励
|
|
state = env.reset() # 重置环境，返回初始状态
|
|
while True:
|
|
action = agent.choose_action(state) # 选择动作
|
|
next_state, reward, done, _ = env.step(action) # 更新环境，返回transition
|
|
agent.memory.push(state, action, reward, next_state, done) # 保存transition
|
|
state = next_state # 更新下一个状态
|
|
agent.update() # 更新智能体
|
|
ep_reward += reward # 累加奖励
|
|
if done:
|
|
break
|
|
if (i_ep+1) % cfg.target_update == 0: # 智能体目标网络更新
|
|
agent.target_net.load_state_dict(agent.policy_net.state_dict())
|
|
rewards.append(ep_reward)
|
|
if ma_rewards:
|
|
ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
|
|
else:
|
|
ma_rewards.append(ep_reward)
|
|
if (i_ep+1)%10 == 0:
|
|
print('回合：{}/{}, 奖励：{}'.format(i_ep+1, cfg.train_eps, ep_reward))
|
|
print('完成训练！')
|
|
return rewards, ma_rewards
|
|
|
|
def test(cfg,env,agent):
|
|
print('开始测试!')
|
|
print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')
|
|
# 由于测试不需要使用epsilon-greedy策略，所以相应的值设置为0
|
|
cfg.epsilon_start = 0.0 # e-greedy策略中初始epsilon
|
|
cfg.epsilon_end = 0.0 # e-greedy策略中的终止epsilon
|
|
rewards = [] # 记录所有回合的奖励
|
|
ma_rewards = [] # 记录所有回合的滑动平均奖励
|
|
for i_ep in range(cfg.test_eps):
|
|
ep_reward = 0 # 记录一回合内的奖励
|
|
state = env.reset() # 重置环境，返回初始状态
|
|
while True:
|
|
action = agent.choose_action(state) # 选择动作
|
|
next_state, reward, done, _ = env.step(action) # 更新环境，返回transition
|
|
state = next_state # 更新下一个状态
|
|
ep_reward += reward # 累加奖励
|
|
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)
|
|
print(f"回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.1f}")
|
|
print('完成测试！')
|
|
return rewards,ma_rewards
|
|
if __name__ == "__main__":
|
|
cfg = DQNConfig()
|
|
plot_cfg = PlotConfig()
|
|
# 训练
|
|
env, agent = env_agent_config(cfg, seed=1)
|
|
rewards, ma_rewards = train(cfg, env, agent)
|
|
make_dir(plot_cfg.result_path, plot_cfg.model_path) # 创建保存结果和模型路径的文件夹
|
|
agent.save(path=plot_cfg.model_path) # 保存模型
|
|
save_results(rewards, ma_rewards, tag='train',
|
|
path=plot_cfg.result_path) # 保存结果
|
|
plot_rewards_cn(rewards, ma_rewards, plot_cfg, tag="train") # 画出结果
|
|
# 测试
|
|
env, agent = env_agent_config(cfg, seed=10)
|
|
agent.load(path=plot_cfg.model_path) # 导入模型
|
|
rewards, ma_rewards = test(cfg, env, agent)
|
|
save_results(rewards, ma_rewards, tag='test',
|
|
path=plot_cfg.result_path) # 保存结果
|
|
plot_rewards_cn(rewards, ma_rewards, plot_cfg, tag="test") # 画出结果
|