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the gradient of the action-value with respect to actions #7
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I think the answer is yes.
First of all, I think it is clear that we are doing a gradient step using the actor's optimizer. I guess the question is more like: "can we propagate gradients to a previous network?" The answer to this is also yes, please refer to: https://discuss.pytorch.org/t/backprop-through-weights-of-a-second-network/52573/4. |
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Hi, I'm not sure if it would calculate the gradient of the action-value with respect to actions?
policy_loss = -self.critic([
to_tensor(state_batch),
self.actor(to_tensor(state_batch))
])
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