Gradient ascent is the same as gradient descent except that you don't multiply your step (learning_rate * gradients) by a negative sign. So your step has the same sign as your gradient.
If the update function defaults to gradient descent, you can adjust the sign of the gradients before updating the parameters.
actorNetwork.Parameters = actorNetwork.Parameters + learningRate * -gradients; (% Perform gradient ascent by adjusting the sign of the gradients)
You can refer to example in this documentation for 'gradient' function
Hope this helps!
