Reinforcement Learning

Reinforcement Learning (RL) is a subfield of machine learning where agents learn policies by trial-and-error and reward feedback to select actions. It models decision-making in sequential environments and suits control, optimization, and planning tasks. Use cases span robotics, game playing, and recommender or scheduling systems.

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Concept

Theoretical construct: explains a term, principle, or mental model.

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In brief

Reinforcement learning in brief

Reinforcement learning learns a policy as an agent acts in states and receives rewards for the consequences of its actions.

Where does it come from?

The paradigm combines behavioral reinforcement and control theory with dynamic programming. Modern methods use neural networks, but still require a well-defined interaction problem.

The mental model

The agent observes a state, chooses an action, and receives a reward and next state. It optimizes accumulated, often delayed reward while balancing exploration with using known good actions. A simulator, safety constraints, and a well-shaped reward are essential.

Key building blocks

Policy

A rule mapping states to actions or action probabilities.

Reward

A signal representing desirable long-term consequences.

Exploration

Trying unknown actions supplies learning information.

When is it useful?

Reinforcement learning fits sequential decisions such as robotics and resource control. Poor rewards, distribution shifts, and unsafe exploration can produce unwanted behavior.

Sutton & Barto – Reinforcement Learning (2nd ed.)