Theoretical remarks on feudal hierarchies and reinforcement learning

Reinforcement learning is a paradigm through which an agent interacts with its environment by trying out different actions at different states and observing the outcome. Each of these interactions can change the state of the environment, and can also provide rewards to the agent. The goal of the agent is to learn the value of performing each action on each state. By value, we mean the biggest amount of rewards that is possible for the agent to obtain after performing that action in that state. If the agent achieves this goal, it can then act optimally on its environment by choosing, at every state, the action that has the biggest value.