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Just to be clear here, your meaning is that the reward function should be differentiable so there is a slope to climb to a maxima (local or gobal). - edit used
by kabouseng 10y ago
Just to be clear here, your meaning is that the reward function should be differentiable so there is a slope to climb to a maxima (local or gobal).
- edit used the wrong word, used derivative meant differentiable.
- habitue 10y agoHe's talking about the operations needing to be differentiable. So like multiplication, addition etc are differentiable. tanh, sigmoid functions are differentiable. You need this because back propagation is an application of the chain rule, so each constituent operation needs to be differentiable. A lot of interesting advances have come from making operations that seems like they aren't differentiable, into differentiable operations that can be trained by backpropagation. See LSTMs and neural turing machines