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johnjim0816
2021-12-22 11:19:13 +08:00
parent c257313d5b
commit 75df999258
55 changed files with 605 additions and 403 deletions
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2
codes/DQN/task0.py
View File

@@ -23,7 +23,7 @@ class DQNConfig:
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.eval_eps = 30 # 测试的回合数
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

2
codes/DQN/task1.py
View File

@@ -26,7 +26,7 @@ class DQNConfig:
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.eval_eps = 30 # 测试的回合数
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

6
codes/DQN/train.ipynb
View File

@@ -180,7 +180,7 @@
" self.algo = \"DQN\" # 算法名称\n",
" self.env = 'CartPole-v0' # 环境名称\n",
" self.train_eps = 200 # 训练的回合数\n",
" self.eval_eps = 20 # 测试的回合数\n",
" self.test_eps = 20 # 测试的回合数\n",
" self.gamma = 0.95 # 强化学习中的折扣因子\n",
" self.epsilon_start = 0.90 # e-greedy策略中初始epsilon\n",
" self.epsilon_end = 0.01 # e-greedy策略中的终止epsilon\n",
@@ -365,7 +365,7 @@
" cfg.epsilon_end = 0.0 # e-greedy策略中的终止epsilon\n",
" rewards = [] # 记录所有回合的奖励\n",
" ma_rewards = [] # 记录所有回合的滑动平均奖励\n",
" for i_ep in range(cfg.eval_eps):\n",
" for i_ep in range(cfg.test_eps):\n",
" ep_reward = 0 # 记录一回合内的奖励\n",
" state = env.reset() # 重置环境，返回初始状态\n",
" while True:\n",
@@ -381,7 +381,7 @@
" else:\n",
" ma_rewards.append(ep_reward)\n",
" if (i_ep+1)%3 == 0: \n",
" print(f\"回合：{i_ep+1}/{cfg.eval_eps}, 奖励：{ep_reward:.1f}\")\n",
" print(f\"回合：{i_ep+1}/{cfg.test_eps}, 奖励：{ep_reward:.1f}\")\n",
" print('完成测试！')\n",
" return rewards,ma_rewards\n",
"\n",

12
codes/DQN/train.py
View File

@@ -5,7 +5,7 @@
@Email: johnjim0816@gmail.com
@Date: 2020-06-12 00:48:57
@LastEditor: John
LastEditTime: 2021-09-15 15:34:13
LastEditTime: 2021-12-22 11:08:04
@Discription:
@Environment: python 3.7.7
'''
@@ -30,13 +30,13 @@ def train(cfg, env, agent):
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())
if (i_ep+1)%10 == 0:
print('回合：{}/{}, 奖励：{}'.format(i_ep+1, cfg.train_eps, 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)
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
@@ -48,7 +48,7 @@ def test(cfg,env,agent):
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.eval_eps):
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:
@@ -63,7 +63,7 @@ def test(cfg,env,agent):
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.eval_eps}，奖励：{ep_reward:.1f}")
print(f"回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.1f}")
print('完成测试！')
return rewards,ma_rewards
@@ -89,7 +89,7 @@ if __name__ == "__main__":
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.eval_eps = 30 # 测试的回合数
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