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update DQN

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This commit is contained in:
JohnJim0816
2020-11-28 10:16:25 +08:00
parent abfe6ea62b
commit 59a09144f1
6 changed files with 41 additions and 39 deletions
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3
codes/dqn/.vscode/settings.json vendored
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@@ -1,3 +0,0 @@
{
"python.pythonPath": "/Users/jj/anaconda3/envs/py37/bin/python"
}

15
codes/dqn/README.md
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@@ -1,3 +1,8 @@
## 思路
见[我的博客](https://blog.csdn.net/JohnJim0/article/details/109557173)
## 环境
python 3.7.9
pytorch 1.6.0
@@ -6,6 +11,7 @@ tensorboard 2.3.0
torchvision 0.7.0
## 使用
train:
@@ -18,7 +24,12 @@ eval:
```python
python main.py --train 0
```
可视化：
```python
tensorboard --logdir logs
```
```
## Torch知识
[with torch.no_grad()](https://www.jianshu.com/p/1cea017f5d11)

22
codes/dqn/agent.py
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@@ -5,7 +5,7 @@
@Email: johnjim0816@gmail.com
@Date: 2020-06-12 00:50:49
@LastEditor: John
LastEditTime: 2020-10-15 21:56:21
LastEditTime: 2020-11-22 11:12:30
@Discription:
@Environment: python 3.7.7
'''
@@ -24,11 +24,12 @@ from memory import ReplayBuffer
from model import FCN
class DQN:
def __init__(self, n_states, n_actions, gamma=0.99, epsilon_start=0.9, epsilon_end=0.05, epsilon_decay=200, memory_capacity=10000, policy_lr=0.01, batch_size=128, device="cpu"):
self.actions_count = 0
self.n_actions = n_actions # 总的动作个数
self.device = device # 设备，cpu或gpu等
self.gamma = gamma
self.gamma = gamma # 奖励的折扣因子
# e-greedy策略相关参数
self.actions_count = 0 # 用于epsilon的衰减计数
self.epsilon = 0
self.epsilon_start = epsilon_start
self.epsilon_end = epsilon_end
@@ -67,12 +68,11 @@ class DQN:
action = random.randrange(self.n_actions)
return action
else:
with torch.no_grad():
with torch.no_grad(): # 取消保存梯度
# 先转为张量便于丢给神经网络,state元素数据原本为float64
# 注意state=torch.tensor(state).unsqueeze(0)跟state=torch.tensor([state])等价
state = torch.tensor(
[state], device='cpu', dtype=torch.float32)
# 如tensor([[-0.0798, -0.0079]], grad_fn=<AddmmBackward>)
[state], device='cpu', dtype=torch.float32) # 如tensor([[-0.0798, -0.0079]], grad_fn=<AddmmBackward>)
q_value = self.target_net(state)
# tensor.max(1)返回每行的最大值以及对应的下标，
# 如torch.return_types.max(values=tensor([10.3587]),indices=tensor([0]))
@@ -86,8 +86,8 @@ class DQN:
# 从memory中随机采样transition
state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(
self.batch_size)
# 转为张量
# 例如tensor([[-4.5543e-02, -2.3910e-01, 1.8344e-02, 2.3158e-01],...,[-1.8615e-02, -2.3921e-01, -1.1791e-02, 2.3400e-01]])
'''转为张量
例如tensor([[-4.5543e-02, -2.3910e-01, 1.8344e-02, 2.3158e-01],...,[-1.8615e-02, -2.3921e-01, -1.1791e-02, 2.3400e-01]])'''
state_batch = torch.tensor(
state_batch, device=self.device, dtype=torch.float)
action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(
@@ -99,9 +99,8 @@ class DQN:
done_batch = torch.tensor(np.float32(
done_batch), device=self.device).unsqueeze(1) # 将bool转为float然后转为张量
# 计算当前(s_t,a)对应的Q(s_t, a)
# 关于torch.gather,对于a=torch.Tensor([[1,2],[3,4]])
# 那么a.gather(1,torch.Tensor([[0],[1]]))=torch.Tensor([[1],[3]])
'''计算当前(s_t,a)对应的Q(s_t, a)'''
'''torch.gather:对于a=torch.Tensor([[1,2],[3,4]]),那么a.gather(1,torch.Tensor([[0],[1]]))=torch.Tensor([[1],[3]])'''
q_values = self.policy_net(state_batch).gather(
dim=1, index=action_batch) # 等价于self.forward
# 计算所有next states的V(s_{t+1})，即通过target_net中选取reward最大的对应states
@@ -119,6 +118,7 @@ class DQN:
self.loss.backward()
for param in self.policy_net.parameters(): # clip防止梯度爆炸
param.grad.data.clamp_(-1, 1)
self.optimizer.step() # 更新模型
def save_model(self,path):

13
codes/dqn/main.py
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@@ -5,7 +5,7 @@
@Email: johnjim0816@gmail.com
@Date: 2020-06-12 00:48:57
@LastEditor: John
LastEditTime: 2020-10-15 22:00:28
LastEditTime: 2020-11-23 11:58:17
@Discription:
@Environment: python 3.7.7
'''
@@ -16,7 +16,7 @@ import argparse
from torch.utils.tensorboard import SummaryWriter
import datetime
import os
from utils import save_results
from utils import save_results,save_model
SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/'
@@ -53,7 +53,7 @@ def get_args():
def train(cfg):
print('Start to train ! \n')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测gpu
env = gym.make('CartPole-v0').unwrapped # 可google为什么unwrapped gym，此处一般不需要
env = gym.make('CartPole-v0')
env.seed(1) # 设置env随机种子
n_states = env.observation_space.shape[0]
n_actions = env.action_space.n
@@ -95,10 +95,7 @@ def train(cfg):
writer.close()
print('Complete training！')
''' 保存模型 '''
if not os.path.exists(SAVED_MODEL_PATH): # 检测是否存在文件夹
os.mkdir(SAVED_MODEL_PATH)
agent.save_model(SAVED_MODEL_PATH+'checkpoint.pth')
print('model saved！')
save_model(agent,model_path=SAVED_MODEL_PATH)
'''存储reward等相关结果'''
save_results(rewards,moving_average_rewards,ep_steps,tag='train',result_path=RESULT_PATH)
@@ -110,7 +107,7 @@ def eval(cfg, saved_model_path = SAVED_MODEL_PATH):
env.seed(1) # 设置env随机种子
n_states = env.observation_space.shape[0]
n_actions = env.action_space.n
agent = DQN(n_states=n_states, n_actions=n_actions, device=device, gamma=cfg.gamma, epsilon_start=cfg.epsilon_start,
agent = DQN(n_states=n_states, n_actions=n_actions, device="cpu", gamma=cfg.gamma, epsilon_start=cfg.epsilon_start,
epsilon_end=cfg.epsilon_end, epsilon_decay=cfg.epsilon_decay, policy_lr=cfg.policy_lr, memory_capacity=cfg.memory_capacity, batch_size=cfg.batch_size)
agent.load_model(saved_model_path+'checkpoint.pth')
rewards = []

14
codes/dqn/plot.py
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@@ -5,7 +5,7 @@
@Email: johnjim0816@gmail.com
@Date: 2020-06-11 16:30:09
@LastEditor: John
LastEditTime: 2020-10-15 22:01:50
LastEditTime: 2020-11-23 13:48:31
@Discription:
@Environment: python 3.7.7
'''
@@ -27,18 +27,6 @@ def plot(item,ylabel='rewards_train', save_fig = True):
plt.savefig(os.path.dirname(__file__)+"/result/"+ylabel+".png")
plt.show()
# def plot(item,ylabel='rewards'):
#
# df = pd.DataFrame(dict(time=np.arange(len(item)),value=item))
# g = sns.relplot(x="time", y="value", kind="line", data=df)
# # g.fig.autofmt_xdate()
# # sns.lineplot(time=time, data=item, color="r", condition="behavior_cloning")
# # # sns.tsplot(time=time, data=x2, color="b", condition="dagger")
# # plt.ylabel("Reward")
# # plt.xlabel("Iteration Number")
# # plt.title("Imitation Learning")
# plt.show()
if __name__ == "__main__":
output_path = os.path.split(os.path.abspath(__file__))[0]+"/result/"

13
codes/dqn/utils.py
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@@ -5,7 +5,7 @@ Author: John
Email: johnjim0816@gmail.com
Date: 2020-10-15 21:28:00
LastEditor: John
LastEditTime: 2020-10-15 21:50:30
LastEditTime: 2020-10-30 16:56:55
Discription:
Environment:
'''
@@ -14,8 +14,17 @@ import numpy as np
def save_results(rewards,moving_average_rewards,ep_steps,tag='train',result_path='./result'):
'''保存reward等结果
'''
if not os.path.exists(result_path): # 检测是否存在文件夹
os.mkdir(result_path)
np.save(result_path+'rewards_'+tag+'.npy', rewards)
np.save(result_path+'moving_average_rewards_'+tag+'.npy', moving_average_rewards)
np.save(result_path+'steps_'+tag+'.npy',ep_steps )
np.save(result_path+'steps_'+tag+'.npy',ep_steps )
print('results saved!')
def save_model(agent,model_path='./saved_model'):
if not os.path.exists(model_path): # 检测是否存在文件夹
os.mkdir(model_path)
agent.save_model(model_path+'checkpoint.pth')
print('model saved！')