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Reinforcement Learning

Definition

Reinforcement learning trains an agent by interaction: actions change an environment, rewards provide feedback and the agent learns a policy intended to maximize cumulative reward.

What is reinforcement learning?

Reinforcement learning is a machine-learning setting in which an agent interacts with an environment. At each step it observes a state, chooses an action, receives a reward and moves to a new state.

What the agent learns

The goal is usually to learn a policy that maximizes expected cumulative reward. Some methods learn value functions that estimate future reward; others optimize policies directly. Q-learning, introduced by Christopher Watkins, is a classic value-based method.

Examples and modern relevance

Reinforcement learning has been used in games, robotics, operations research and control. Preference-based post-training of language models is related but not identical to an agent learning directly in a physical environment. In both cases, a reward signal influences behavior.

Related terms, defined

Classical AI & Machine Learning Supervised Learning Supervised learning trains a model from examples paired with known labels or target values. Classification and regression are common supervised tasks. Classical AI & Machine Learning Unsupervised Learning Supervised learning trains a model from examples paired with known labels or target values. Classification and regression are common supervised tasks. Classical AI & Machine Learning Artificial Intelligence vs Machine Learning Artificial intelligence is the broader field concerned with systems performing tasks associated with intelligent behavior; machine learning is a major subfield in which systems learn patterns from data. Classical AI & Machine Learning Symbolic AI Symbolic AI represents knowledge with explicit symbols, rules and structures, then manipulates those representations with logic, search or planning procedures. Classical AI & Machine Learning Search Algorithms in Artificial Intelligence AI search algorithms explore possible states or actions in order to find paths, plans or solutions. Classical examples include breadth-first search, depth-first search and A*.

Reference guide and primary sources

Wikipedia is used here as a terminology and history reference guide. Current model versions, institutional statistics and product-specific claims are also linked to first-party or institutional sources because those details can change faster than encyclopedia articles.

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