Multi-Objective Reinforcement Learning

Multi-Objective Reinforcement Learning (MO-RL) is a machine learning approach designed to optimize multiple objectives in complex environments. Unlike traditional single-objective reinforcement learning, MO-RL aims to handle multiple, potentially conflicting goals simultaneously by learning optimal policies through the interaction of agents with the environment. This method achieves more comprehensive performance optimization by balancing the trade-offs between different objectives, and it is widely applied in scenarios such as resource management, robot navigation, and financial decision-making, where multi-objective optimization is crucial.