I am training a network through reinforcement learning. The policy network learns rotations, but depending on the actual input (state), the output of the network should be restricted to be in certain bounds otherwise it mostly fails to reach these bounds. I am using
tanh as last activation function. So, I wonder if there could be a way to modify this last activation function s.th. it can adaptively change bounds depending on input? Or would this have a negative impact in learning?
I would also be open for papers or publications tackling these kind of problems. Thank you for your help!