Reinforcement learning is a paradigm of machine learning where an agent learns to make sequences of decisions by interacting with an environment and receiving feedback in the form of rewards or punishments​.

The learning is trial-and-error based: the agent takes an action in a given state, the environment responds with a new state and a reward signal, and the agent updates its policy (behavior strategy) accordingly. Over time, the agent’s objective is to learn a policy that maximizes cumulative reward. Key concepts in RL include states (the situation the agent is in), actions (choices the agent can make), rewards (numeric feedback), and the policy (mapping from states to actions)​.

RL algorithms can be model-free (learning directly from experience, e.g., Q-learning, policy gradients) or model-based (using a model of the environment’s dynamics). This approach has achieved notable success in areas like game playing (AlphaGo), robotics, and resource management.