I realize I choose to go that way because it would fit the way I want to frame my problem, yet I could have asked help broadening a bit the explanation about that.
So, I’m kind of dealing with reinforcement learning (“Kind of” because my challenge is to use as much shortcuts as possible, so no fancy Markovian formalism).
My agent (for the part I’m currently struggling to solve anyway) can decide to put ON of OFF a state. Matrix a represents the neuron “turn it on”, and b “turn it off”.
The resulting matrix should be the resulting state.
As following operations is fairly straightforward (Matrix multiplication, error function, gradient descent), doing that one in few GPU operations would be a tremendous win.
Edit:
Hence, another way to express illustratively what I’d like to achieve:
#I want to turn:
start_walking = torch.tensor([1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0])
stop_walking = torch.tensor([0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0])
#Into
> is_walking tensor([1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1])