My question concerns a side question
(which was not answered) asked here:
How can policy gradients be applied in the case of multiple continuous actions?
I am trying to implement a simple policy gradient algorithm for a discrete multi-action reinforcement learning task. To be more precise, there are three actuators. At every time step, each of the actuators can perform one of three possible actions.
Is it possible to adjust the loss function from the single action case per time step
$$L = \log(P(a_1)) A$$
to the n-action case per time step like so?
$$L = (\log(P(a_1)) + \log(P(a_2))+ \dots + \log(P(a_n))) A$$?