What Is Reinforcement Learning? Core Concepts Explained

Reinforcement Learning Basics

Reinforcement learning (RL) has become increasingly popular in the machine learning field. Originating in the 1980s and inspired by behavioral psychology, RL focuses on a decision maker (agent) interacting with an environment to maximize cumulative reward. Unlike supervised learning, RL does not provide direct labels; the agent receives indirect feedback and must improve its policy through trial and error. RL applies to many dynamic decision‑making problems, including game theory, control, optimization, AlphaGo, robotics, and autonomous driving.

The basic RL scenario consists of an environment, an agent, states, actions, and rewards. The agent takes actions, the environment responds with a new state and a reward, and the agent aims to choose actions that maximize its total return.

The interaction can be formalized as a Markov Decision Process (MDP). Its main elements are:

Action (A): the set of all possible actions.

State (S): the set of all possible states.

Reward (R): a scalar feedback signal received after each action.

The core task of RL is to learn a mapping from states (S) to actions (A) that maximizes cumulative benefit. Common RL algorithms include Q‑learning, policy gradient methods, and actor‑critic approaches.