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标量函数backward import torch from torch.autograd import Variable import torch.nn as nn import torch.nn.functional as F #反向传播 x = torch.ones(2, 2, require 阅读全文
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import torch from torch.autograd import Variable import torch.nn as nn import torch.nn.functional as F import torch.optim as optim # 定义网络 class Net(nn 阅读全文
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import torch from torch.autograd import Variable import torch.nn as nn import torch.nn.functional as F #反向传播 x = torch.ones(2, 2, requires_grad=True) 阅读全文
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参考博主:https://blog.csdn.net/weixin_41457494/article/details/86238443 import torch from torch.autograd import Variable import torch.nn as nn import torc 阅读全文
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value iteration和policy iteration的区别 value iteration: ①多次迭代Bellman最优等式和Bellman等式,等价值函数收敛后,②再用价值函数带入贝尔曼等式得到动作价值函数,策略就从最大的动作价值函数选取。(策略没有参与) policyiterati 阅读全文
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满射 A mapping \(T: \mathbb{R}^{n} \rightarrow \mathbb{R}^{m}\) is said to be onto \(\mathbb{R}^{m}\) if each \(\mathbf{b}\) in \(\mathbb{R}^{m}\) is th 阅读全文