# How to let the agent choose how to populate a state space matrix in RL (using python)

I have an agent (drone) that has to allocate subchannels for different types of User Equipment.

I have represented the subchannel allocation with a 2-dimentional binary matrix, that is initialized to all zeros as there is no requests at the beginning of the episode.

When the agent chooses an action, it has to choose which subchannels to allocate to which UEs, hence populating the matrix with 1s.

I have no idea how to do it.

• Can you please be a little more intuitive on what you would like to do? What is a subchannel, UE, and in general what a human being would face in this problem. Describe a use case scenario. This would help to define our env, space and actions. Sep 28, 2020 at 9:47
• To extend ddaedalus' comment, it would help to understand what the goals of the allocation are, and what the difference is between good and bad allocations Jun 22 at 11:10

The simplest strategy is to use the so called epsilon-greedy strategy (or $$\epsilon$$-greedy). This means that you select an action at random $$x$$ percent of the times that an agent has to select an action. The other times, the agent takes the action that its current policy dictates. Usually, $$x$$ declines throughout the learning process.