TREX: Trajectory Explanations for Multi-Objective Reinforcement Learning

ArXi:2603.21988v1 Announce Type: cross Reinforcement Learning (RL) has nstrated its ability to solve complex decision-making problems in a variety of domains, by optimizing reward signals obtained through interaction with an environment. However, many real-world scenarios involve multiple, potentially conflicting objectives that cannot be easily represented by a single scalar reward. Multi-Objective Reinforcement Learning (MORL) addresses this limitation by enabling agents to optimize several objectives simultaneously, explicitly reasoning about trade-offs between them.