I started to learn reinforcement learning a few days ago. And I want to use that to solve resource allocation problem something like given a constant number, find the best way to divide it into several real numbers each is non-negative.
For example, to divide the number 1 into 3 real numbers, the allocation can be:
[0.2, 0.7, 0.1]
[0.95, 0.05, 0] ...
I do not know how to represent the action space because each allocation is 3-dimensional and each dimension is real-valued and each other correlated.
In actor-critic architecture, is it possible to have 3 outputs activated by softmax in the actor's network each represents one dimension in the allocation?
There is a playlist of videos. A user can switch to the next video at any time. More buffer leads to better viewing experience but more bandwidth loss if user switches to the next video. I want to optimize the smoothness of playback with minimal bandwidth loss. At each time step, the agent decides the bandwidth allocation to download current video and the next 2 videos. So I guess the state will be the bandwidth, user's behavior and the player situation.