Multi Objective Optimization

A Practical Guide to Multi-Objective Reinforcement Learning and Planning

A Springer survey on multi-objective reinforcement learning and planning — covers scalarization, Pareto-based methods, and utility-based approaches for agents that must balance competing rewards. Useful reference for RL research where single-reward framing is inadequate.