RL algorithms adhere to the principles of Markov Decision Process and operate on states, actions, and rewards. In a given state, a decision is chosen for this state and then the corresponding action is taken. By taking this action, a reward is obtained (positive, negative, or zero), and the state is updated according to the underlying dynamics of the RL problem. The goal of RL is to obtain policies, i.e., (possibly stochastic) mappings from states to actions, which maximise the expected long-term aggregated reward. There is a direct one-to-one correspondence between the actions in RL-CSP and those in the corresponding underlying BH code.