Reinforcement Learning

Mon, 01 Jan 0001 00:00:00 +0000

Reinforcement Learning #

Reinforcement Learning (RL) is a learning approach in which an agent improves its behaviour by interacting with an environment and observing the rewards produced by its actions.

Unlike supervised learning, the agent is not given a correct action label for every situation. It must discover useful behaviour through trial, feedback, and repeated interaction.

Reinforcement learning is goal-oriented learning through interaction.

Why Reinforcement Learning? #

RL is useful when decisions affect what happens next and the quality of an action may only become clear later.

Multi-Armed Bandit Problem

Mon, 01 Jan 0001 00:00:00 +0000

Multi-Armed Bandit Problem #

The Multi-Armed Bandit (MAB) problem is the simplest setting for studying decision-making under uncertainty.

An agent repeatedly chooses one of \( k \) actions. Each action produces a numerical reward drawn from an unknown distribution. The objective is to maximise the expected total reward over time.

The central challenge is deciding when to exploit current knowledge and when to explore uncertain alternatives.

The k-Armed Bandit Problem ☆ #