Which two components are essential in Reinforcement Learning?

In Reinforcement Learning (RL), the fundamental components revolve around the interaction of an agent with its environment to achieve a goal through trial and error. Essential to this process are the concepts of actions and the results or feedback from those actions.

When an agent takes an action in a given environment, it receives feedback in the form of rewards or penalties that it uses to learn over time. This feedback is critical because it drives the learning process, allowing the agent to understand which actions yield positive outcomes (rewards) and which do not (negative outcomes). The ultimate goal is to develop a strategy or policy that maximizes cumulative rewards through autonomous decision-making.

The concept of "autonomous decisions" highlights the agent's ability to make decisions without human intervention, relying instead on the results (or outcomes) of its previous actions to inform future choices. Understanding this interaction is crucial to the effectiveness of the reinforcement learning process, as it directly influences the agent’s ability to optimize its behavior in complex environments.

This makes the combination of autonomous decisions and action results fundamental to the Reinforcement Learning paradigm, as they encapsulate both the decision-making and learning components necessary for an agent to adapt and succeed in various contexts.