Multiple objectives stop being extra baggage: each agent learns its own preference, and the dynamic variant tops the Pareto front on four cooperative tasks
Multi-objective multi-agent reinforcement learning has treated multiple objectives as an extra optimisation dimension stacked on the multi-agent problem. MIRL instead uses that objective structure to guide cooperation: a team preference fixes the system-level trade-off while each agent carries a learnable individual preference, adjusted by meta-gradient according to subsequent team performance. On four multi-objective particle-environment tasks, MIRL-S and MIRL-D improve Pareto-front quality over five baselines, with the dynamic variant MIRL-D strongest overall.