The Multi-armed Bandit Problem is a classic problem in decision theory and reinforcement learning that explores the trade-off between exploration and exploitation to maximize rewards. It models scenarios where you must choose between multiple options with uncertain payoffs, akin to selecting which arm of a slot machine to pull to achieve the highest cumulative reward over time.

Multi-armed Bandit Problem

Decision theory is a framework for making logical choices in the face of uncertainty, integrating principles from statistics, economics, and psychology to evaluate and optimize decisions. It encompasses both normative theories, which prescribe how decisions should be made, and descriptive theories, which describe how decisions are actually made by individuals and organizations.

decision theory

Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative reward. It involves trial and error, exploration, and exploitation to develop an optimal strategy or policy for decision-making tasks.

reinforcement learning

trade-off between exploration and exploitation

maximize rewards

uncertain payoffs

slot machine

highest cumulative reward

Exploration is the act of traveling through unfamiliar areas to discover new information, resources, or territories, often driven by curiosity, necessity, or ambition. It plays a critical role in expanding human knowledge and understanding, influencing cultural exchange, scientific advancement, and economic development.

exploration

Exploitation refers to the unfair use of someone or something for one's own advantage, often without proper compensation or consideration of the exploited party's welfare. It is a central issue in discussions about labor rights, economic inequality, and ethical business practices, highlighting power imbalances and the need for equitable treatment.

Relevant Degrees

Log in to see lessons