Multi Objective Reinforcement Learning

Multi-objective reinforcement learning (MORL) tackles the challenge of training agents to optimize multiple, often conflicting, objectives simultaneously. Current research focuses on developing efficient algorithms to discover the Pareto front—the set of optimal trade-offs between objectives—and improving the generalization of learned policies to unseen preferences, employing techniques like constrained optimization, preference inference from demonstrations, and novel architectures such as hypernets and diffusion models. These advancements are significant for addressing complex real-world problems in robotics, resource management, and other domains where single-objective approaches are insufficient, leading to more robust and adaptable AI systems.