leao1995
leao1995
https://github.com/thu-ml/tianshou/blob/f13e415eb0de55baca5dc0d6fae39d6a38e8bc0b/tianshou/policy/modelfree/dqn.py#L167 It seems `state` is not used during training even when specifying a recurrent net. Am I missing something, or is it expected?
https://github.com/lilianweng/deep-reinforcement-learning-gym/blob/4fec4876ad28fe83309efd2cdf2a6f4281a5b23c/playground/policies/ddpg.py#L47 a is rescaled, but mu is not.
https://github.com/openai/glow/blob/eaff2177693a5d84a1cf8ae19e8e0441715b82f8/model.py#L552 this is equivalent to evaluate (z-mean)/scale on a standard Gaussian, but you didn't account for the determinant of this transformation.
I want to use 3D version of deformable convolution, but it seems like your code only support 2D. What change should be made to adapt to 3D convolution? Is it...
would it be better to let batch norm parameters adapt to your current data?
https://github.com/OpenXAIProject/Joint-AFA-Classification/blob/55811e71376ed6d90a4f2012694cb3891ac48a56/environment.py#L87 should be len(np.where(...)[0]) here.