Payoff Function

Payoff functions, which map actions or outcomes to numerical values representing rewards or costs, are central to decision-making in various fields, from game theory and reinforcement learning to online advertising and finance. Current research focuses on extending payoff function models to handle complexities like partial observability, delayed rewards, and multi-objective scenarios, often employing algorithms such as Thompson sampling, mirror descent, and evolutionary search within frameworks like contextual bandits and multi-armed bandits. These advancements are improving the design of efficient and robust algorithms for decision-making under uncertainty and competition, with implications for resource allocation, online learning, and the analysis of strategic interactions.