虽然说很多代码都有问题，但是不管它们，我不是为了去debug，紧盯住自己的目标，目标是整理出一条通常的强化学习之路，让自己以及看到这些博客的大家在学习的时候能够少走一些弯路。所以从q-learning和Sarsa开始，这些基础代码不需要借助框架，所以没什么太大问题。但是深度学习的话就要借助TensorFlow或者pytorch框架，而这两个框架都分别出了两个版本，就导致前后的兼容性较差，前人的经验工作无法得到有效的利用。

Deep-Reiforcement-Learning之debug旅程

我找了许多的学习代码，但是很多经常不能使用，但是我也花费了许多功夫，所以现在我想直接把对这些代码的debug过程记录下来。好了，话不多说，下面看着这么多的代码，又有几个能用的呢？测试一下吧

果不其然，运行code6-2

C:\Users\admin\AppData\Local\Programs\Python\Python37\lib\site-packages\gym\envs\registration.py:556: UserWarning: WARN: The environment CartPole-v0 is out of date. You should consider upgrading to version `v1`.
  f"The environment {id} is out of date. You should consider "
C:\Users\admin\AppData\Local\Programs\Python\Python37\lib\site-packages\numpy\lib\shape_base.py:591: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  a = asanyarray(a)
Traceback (most recent call last):
  File "C:/Python program/Deep-Reiforcement-Learning-main/code/Code6/code6-2 DQN-2015算法求解倒立摆问题代码.py", line 220, in <module>
    agent.train()                   #训练智能体
  File "C:/Python program/Deep-Reiforcement-Learning-main/code/Code6/code6-2 DQN-2015算法求解倒立摆问题代码.py", line 145, in train
    action = self.egreedy_action(state) 
  File "C:/Python program/Deep-Reiforcement-Learning-main/code/Code6/code6-2 DQN-2015算法求解倒立摆问题代码.py", line 70, in egreedy_action
    state = torch.from_numpy(np.expand_dims(state,0))
TypeError: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool.

大概就是环境不对，好，改成v1
再运行

C:\Users\admin\AppData\Local\Programs\Python\Python37\lib\site-packages\numpy\lib\shape_base.py:591: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  a = asanyarray(a)
Traceback (most recent call last):
  File "C:/Python program/Deep-Reiforcement-Learning-main/code/Code6/code6-2 DQN-2015算法求解倒立摆问题代码.py", line 220, in <module>
    agent.train()                   #训练智能体
  File "C:/Python program/Deep-Reiforcement-Learning-main/code/Code6/code6-2 DQN-2015算法求解倒立摆问题代码.py", line 145, in train
    action = self.egreedy_action(state) 
  File "C:/Python program/Deep-Reiforcement-Learning-main/code/Code6/code6-2 DQN-2015算法求解倒立摆问题代码.py", line 70, in egreedy_action
    state = torch.from_numpy(np.expand_dims(state,0))
TypeError: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool.

Process finished with exit code 1

依然错误，好我再修改

state = torch.from_numpy(np.expand_dims(state, 0)).float()

然后运行，还是错误
然后运行其他的代码，也没有一个能跑的，算了

Reinforcement-learning-with-tensorflow代码debug之旅

差点忘了，这个前几天刚debug过，里面深度强化学习的代码基本上都由于版本问题无法跑起来，放弃

pytorch-tutorial代码debug之旅，这个代码看起来也挺全的，但是就不知道能不能跑

太好了，终于遇见一个能打的了，也感恩pytorch，TensorFlow实在是非常坑，我是一百个不愿意用它

作图，matplotlib的使用

import torch
import torch.nn.functional as F
from torch.autograd import Variable
import matplotlib.pyplot as plt

x=torch.linspace(-5,5,200)
x=Variable(x)
x_np=x.data.numpy()

y_relu=torch.relu(x).data.numpy()
y_sigmoid=torch.sigmoid(x).data.numpy()
y_tanh=torch.tanh(x).data.numpy()
y_softplus=F.softplus(x).data.numpy()

#画图
plt.figure(1,figsize=(8,6))

plt.subplot(221)
plt.plot(x_np,y_relu,c='red',label='relu')
plt.ylim((-1,5))
plt.legend(loc='best')

plt.subplot(222)
plt.plot(x_np,y_sigmoid,c='red',label='relu')
plt.ylim((-0.2,1.2))
plt.legend(loc='best')

plt.subplot(223)
plt.plot(x_np,y_tanh,c='red',label='tanh')
plt.ylim((-1.2,1.2))
plt.legend(loc='best')

plt.subplot(224)
plt.plot(x_np,y_softplus,c='red',label='softmax')
plt.ylim((-0.2,6))
plt.legend(loc='best')

plt.show()

神经网络还必须得GPU才能跑

import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt
from torch.utils.tensorboard import SummaryWriter
import os
log_dirs="logs/regre"
writer=SummaryWriter(log_dir=log_dirs)
x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)  # x data (tensor), shape=(100, 1)
y = x.pow(2) + 0.2*torch.rand(x.size())                 # noisy y data (tensor), shape=(100, 1)

class Net(torch.nn.Module):
    def __init__(self, n_feature, n_hidden, n_output):
        super(Net, self).__init__()
        self.hidden = torch.nn.Linear(n_feature, n_hidden)   # hidden layer
        self.predict = torch.nn.Linear(n_hidden, n_output)   # output layer

    def forward(self, x):
        x = F.relu(self.hidden(x))      # activation function for hidden layer
        x = self.predict(x)             # linear output
        return x

net = Net(n_feature=1, n_hidden=10, n_output=1)     # define the network
print(net)  # net architecture

optimizer = torch.optim.SGD(net.parameters(), lr=0.2)
loss_func = torch.nn.MSELoss()  # this is for regression mean squared loss

plt.ion()   # something about plotting

for t in range(200):
    prediction = net(x)     # input x and predict based on x

    loss = loss_func(prediction, y)     # must be (1. nn output, 2. target)

    optimizer.zero_grad()   # clear gradients for next train
    loss.backward()         # backpropagation, compute gradients
    optimizer.step()        # apply gradients
    writer.add_scalar("Loss",loss)

    if t % 5 == 0:
        # plot and show learning process
        plt.cla()
        plt.scatter(x.data.numpy(), y.data.numpy())
        plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)
        plt.text(0.5, 0, 'Loss=%.4f' % loss.data.numpy(), fontdict={'size': 20, 'color':  'red'})
        plt.pause(0.1)

writer.close()
plt.ioff()
plt.show()

上面是一个用神经网络的回归代码，我又添加了tensorboard查看网络的代码，结果如下：

pytorch中使用tensorboard的方法
1.导入所需的库：

from torch.utils.tensorboard import SummaryWriter

2.创建一个SummaryWriter对象：

writer = SummaryWriter()

3.在训练过程中，将需要记录的指标（例如损失、准确率等）写入到SummaryWriter对象中：

# 在训练循环中，例如每个epoch或每个iteration
writer.add_scalar('Loss', loss, global_step)
writer.add_scalar('Accuracy', accuracy, global_step)

4.在训练完成后，关闭SummaryWriter对象：

writer.close()

5.启动TensorBoard服务器：
在终端中，使用以下命令在指定的目录中启动TensorBoard服务器：

tensorboard --logdir=logs

6.在浏览器中查看TensorBoard的可视化结果：
打开浏览器，访问TensorBoard服务器的地址（默认是http://localhost:6006），即可查看训练过程和模型性能的可视化结果。

logs是一个目录路径，你可以在你的代码中自行定义。它用于存储TensorBoard日志文件和事件文件，供TensorBoard服务器读取并进行可视化。

通常，你可以将logs目录定义为你项目的根目录下的一个子目录，或者在任意合适的位置创建一个新的目录。在定义logs路径时，你可以使用绝对路径或相对路径。

以下是一个示例，将logs目录定义为项目根目录下的子目录：

logs_dir = os.path.join(os.getcwd(), 'logs')
writer = SummaryWriter(log_dir=logs_dir)

OK了