Heterogeneous Reward

Heterogeneous reward, a challenge in reinforcement learning and related fields, arises when different agents or scenarios exhibit varying reward structures. Current research focuses on developing algorithms that efficiently handle this variability, including variance-adaptive methods that allocate resources based on reward uncertainty and algorithms that model heterogeneous reward functions directly, often employing techniques like expectation-maximization. These advancements are crucial for improving the performance of systems in complex environments where reward structures are not uniform, with applications ranging from personalized education to multi-agent collaboration and optimal resource allocation.