Reinforcement learning is a branch of machine learning where an agent learns to make sequences of decisions by interacting with an environment and receiving rewards or penalties. The central idea is trial and error: the agent explores possible actions, observes the consequences, and gradually improves its behavior to maximize cumulative reward.

Formally, the problem is often modeled as a Markov decision process, defined by states, actions, transition probabilities and a reward function. The agent’s goal is to learn a policy that maps states to actions to maximize expected return. Key concepts include value functions, which estimate future rewards, and the balance between exploration of new actions and exploitation of known good ones.

Several core algorithmic families drive progress in this field. Dynamic programming methods solve problems when a full model of the environment is known. Temporal difference learning, such as Q learning and SARSA, learns directly from experience without needing such a model. Policy gradient methods directly adjust the policy’s parameters to improve performance.

The fusion of reinforcement learning with deep neural networks has enabled agents to operate on high dimensional inputs, like raw images, and master complex tasks such as Atari games, Go and robotics control. Research explores stability, sample efficiency, safety, generalization and multi agent interactions, as well as applications in recommendation systems, resource management and scientific discovery.

Current work also investigates ways to incorporate prior knowledge, structure rewards, and learn from limited or offline data. Reinforcement learning thus serves both as a practical toolkit for decision making under uncertainty and as a framework for studying learning and intelligence.