Convex Reward

Convex reward, in the context of reinforcement learning and online optimization, refers to scenarios where the objective is to maximize a reward function that exhibits desirable mathematical properties, often related to concavity or submodularity. Current research focuses on extending these methods beyond simple additive rewards to handle complex interactions between actions or states, employing techniques like submodular optimization and online learning algorithms (e.g., Frank-Wolfe methods) to achieve low regret. This work is significant because it enables the modeling of diverse real-world problems, including resource allocation, principal-agent interactions, and decentralized exchange mechanisms, where traditional RL approaches fall short.