Bayesian regret

Sat, 18 Jul 2026 09:48:06 +0000

Definition

Bayesian regret quantifies the difference between the optimal reward achievable with perfect information and the expected reward obtained by an agent acting under uncertainty. It is calculated by integrating the regret over all possible states of the world weighted by their prior probabilities. This concept is crucial in reinforcement learning and game theory, helping to evaluate how well an algorithm performs when it must make decisions without knowing the true underlying parameters or environment dynamics.

Decision Theory on English AI Terms Dictionary

Bayesian regret

Definition