arXiv:2605.31388v1 Announce Type: new Abstract: Multi-Objective Reinforcement Learning (MORL) extends standard RL by optimizing policies with respect to multiple, often conflicting, objectives. While max-min MORL has emerged as an effective approach for promoting fairness, its applicability remains limited, particularly when constraints must be incorporated. In this paper, we propose a MORL framework that integrates the max-min criterion with explicit constraint satisfaction. We establish a theoretical foundation for the proposed framework and validate the resulting algorithm through convergen
The increasing complexity of real-world AI applications, particularly those involving multiple objectives and safety constraints, drives the need for more sophisticated control frameworks like constrained multi-objective reinforcement learning.
This research provides a theoretical and algorithmic foundation for developing AI systems that can balance multiple, often conflicting, goals while adhering to critical safety and operational constraints, enhancing their deployability in sensitive domains.
The ability to integrate fairness (max-min criterion) with explicit constraint satisfaction in MORL expands the practical applicability of reinforcement learning, moving towards more robust and ethically aligned autonomous agents.
- · AI developers
- · Robotics industry
- · Safety-critical AI applications
- · Autonomous systems
- · Simple reinforcement learning approaches
Improved fairness and safety guarantees in AI decision-making systems become possible.
Broader adoption of reinforcement learning in regulated and real-world environments with high stakes.
Enhanced trust in AI systems leading to a faster integration into critical infrastructure and public life.
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Read at arXiv cs.LG