use negative loss in tensorflow
A gradient descent minimizer will typically try to find the minimum loss irrespective of the sign of the loss surface.  It sounds like you either want to a) assign a large loss to encourage your model to pick something else or b) assign a fifth no-action category.
I am implementing a reinforcement agent that takes actions based on classes.
so it can take action 1 or 2 or 3 or 4.
So my question is can I use negative loss in tensorflow to stop it from outputting an action.
Example:
Let's say the agent outputs action 1 I want to very strongly dissuade it from taking action 1 in that situation again.  but there is not a known action that it should have taken instead.  So I can't just choose a different action to make it learn that.
So my question is:
does tensorflow gradient computation handle negative values for loss.
And if it does will it work the way I describe?