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johnjim0816
2021-12-29 15:06:20 +08:00
parent bd51b5a7ad
commit 89abbc5ebb
31 changed files with 108 additions and 115 deletions
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codes/DoubleDQN/task0.py
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@@ -5,7 +5,7 @@ Author: JiangJi
Email: johnjim0816@gmail.com
Date: 2021-11-07 18:10:37
LastEditor: JiangJi
LastEditTime: 2021-11-19 18:34:05
LastEditTime: 2021-12-29 15:02:30
Discription:
'''
@@ -20,20 +20,22 @@ import datetime
from common.utils import save_results, make_dir
from common.utils import plot_rewards
from DoubleDQN.agent import DoubleDQN
from DoubleDQN.train import train,test
from DoubleDQN.double_dqn import DoubleDQN
curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
algo_name = 'DoubleDQN' # 算法名称
env_name = 'CartPole-v0' # 环境名称
class DoubleDQNConfig:
class Config:
def __init__(self):
self.algo_name = algo_name # 算法名称
self.env_name = env_name # 环境名称
################################## 环境超参数 ###################################
self.algo_name = 'DoubleDQN' # 算法名称
self.env_name = 'CartPole-v0' # 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
################################################################################
################################## 算法超参数 ###################################
self.gamma = 0.95 # 强化学习中的折扣因子
self.epsilon_start = 0.95 # e-greedy策略中初始epsilon
self.epsilon_end = 0.01 # e-greedy策略中的终止epsilon
@@ -43,20 +45,16 @@ class DoubleDQNConfig:
self.batch_size = 64 # mini-batch SGD中的批量大小
self.target_update = 2 # 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
################################# 保存结果相关参数 ##############################
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
self.save = True # 是否保存图片
################################################################################
def env_agent_config(cfg,seed=1):
env = gym.make(cfg.env_name)
@@ -66,18 +64,81 @@ def env_agent_config(cfg,seed=1):
agent = DoubleDQN(state_dim,action_dim,cfg)
return env,agent
cfg = DoubleDQNConfig()
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(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(rewards, ma_rewards, plot_cfg, tag="test") # 画出结果
def train(cfg,env,agent):
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)
next_state, reward, done, _ = env.step(action)
ep_reward += reward
agent.memory.push(state, action, reward, next_state, done)
state = next_state
agent.update()
if done:
break
if i_ep % cfg.target_update == 0:
agent.target_net.load_state_dict(agent.policy_net.state_dict())
if (i_ep+1)%10 == 0:
print(f'回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward}')
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)
print('完成训练！')
env.close()
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):
state = env.reset()
ep_reward = 0
while True:
action = agent.choose_action(state)
next_state, reward, done, _ = env.step(action)
state = next_state
ep_reward += reward
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！')
env.close()
return rewards,ma_rewards
if __name__ == "__main__":
cfg = Config()
# 训练
env, agent = env_agent_config(cfg)
rewards, ma_rewards = train(cfg, env, agent)
make_dir(cfg.result_path, cfg.model_path) # 创建保存结果和模型路径的文件夹
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(rewards, ma_rewards, cfg, tag="train") # 画出结果
# 测试
env, agent = env_agent_config(cfg)
agent.load(path=cfg.model_path) # 导入模型
rewards, ma_rewards = test(cfg, env, agent)
save_results(rewards, ma_rewards, tag='test',
path=cfg.result_path) # 保存结果
plot_rewards(rewards, ma_rewards, cfg, tag="test") # 画出结果