用多智能体学习自动调节权重，解决动态环境中约束优化的平衡难题。
A Multi-Agent system for Multi-Objective constrained optimization
- 将权重选择变成可学习任务，解耦目标设计与执行
- 在动态环境中自动平衡成本最小化与约束违反避免
- 适合需要自适应调整的实时决策系统
计算与网络系统中的许多决策问题可自然建模为在性能约束下的成本最小化问题。在动态环境中，通常采用强化学习（RL）通过加权惩罚项将成本和约束违反合并为单一标量奖励来求解，遵循拉格朗日启发式方法。然而，此方法中学习策略的行为高度依赖于权重选择，而这些权重通常需手动设定。这使得在优化主目标与有效避免约束违反之间难以找到合适折衷，尤其在相对重要性可能变化的非平稳环境中。本文提出MAMO（多智能体系统用于多目标约束优化），通过多智能体强化学习解决这一平衡问题。MAMO通过将奖励权重的选择建模为学习问题，实现任务执行与目标设计的解耦，为动态环境中基于强化学习的约束优化提供了更自主、鲁棒的第一步解决方案。
原文摘要 · Abstract (English)
Many decision-making problems in computing and networking systems can be naturally formulated as cost-minimization problems under performance constraints. In dynamic environments, reinforcement learning (RL) is often used to solve such problems at runtime by embedding both costs and constraint violations into a single scalar reward through weighted penalty terms, following a Lagrangian-inspired formulation. However, in this context the behavior of the learned policy critically depends on the choice of these weights, which are typically selected manually. This makes it difficult to identify an appropriate trade-off between optimizing the primary objective and effectively avoiding constraint violations, particularly in non-stationary environments where their relative importance may change. This paper presents MAMO (Multi-Agent system for Multi-Objective constrained optimization), an approach to tackle this balancing problem through multi-agent RL. MAMO decouples task execution from objective design by formulating the selection of reward weights as a learning problem, providing a !rst step towards more autonomous and robust RL-based solutions for constrained optimization problems in dynamic environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。