I’m developing an application using Reinforcement Learning (RL) where my agent can choose between three different algorithms (actions) to determine its set of motions for achieving a task. These algorithms vary in their memory usage and the time they take to generate a solution. The goal of the agent is to consume the least memory and solving time via executing the solution provided by the algorithm based on its current state, thus the agent can switch from one algorithm to another at each step if necessary.