Multi-Objective Reinforcement Learning Using Routed Ensembles and Trajectory Attribution

Multi-objective reinforcement learning (MORL) is a fundamental problem for modern AI, with applications spanning robotics, LLMs, and reasoning. A central challenge is to learn policies that balance competing objectives, such as speed versus energy in robotics, or helpfulness versus honesty in alignment. Optimal policies can vary sharply with the preferred objective weights, and the severity of this variation depends crucially on the degree of misalignment among objectives, making per-preference learning sample-inefficient. To formalize this, we introduce an objective-conflict coefficient and use it to derive near-optimal regret bounds for online MORL that quantify the impact of misalignment on learning efficiency. Motivated by this analysis, we propose routed ensemble policy learning , an algorithm that maintains a small set of basis policies and dynamically routes trajectories to the policy with the highest potential reward. We further design a scalable, kernel-based attribution algorithm that estimates how feedback signals from one set of trajectories influence policy learning on others. Unlike existing attribution methods, our approach captures nonlinear interactions, such as synergy and antagonism between trajectories, to explain objective conflict. We extensively evaluate our approach across diverse environments and benchmarks: routed ensembles improve average performance by 4.3% over MORL baselines on several environments from the Meta-World and MO-Gymnasium benchmarks, and kernel-based attribution improves correlation with leave-one-out analysis over existing baselines by 50%. When applied to downstream task selection, kernel-based attribution yields a 27% improvement in reward over baseline attribution methods.