Multi Objective Reward

Multi-objective reward in reinforcement learning addresses the challenge of optimizing multiple, potentially conflicting, goals simultaneously. Current research focuses on developing reward functions that effectively balance these objectives, often employing deep reinforcement learning architectures like hierarchical models and graph neural networks, along with techniques such as model averaging and multi-objective reward exponentials to improve performance and mitigate issues like reward sparsity and the "alignment tax" in RLHF. This work is crucial for advancing the capabilities of AI systems in complex environments, enabling more nuanced control over agent behavior and improved alignment with human preferences in applications ranging from robotics to large language models.