Title: Orchestrating Pre-Trained Agents for Multi-Objective Decision Making
Primary area: reinforcement learning
Keywords: Multi-Objective Optimization, Context Engineering, LLM-judge, ML Reusability

Abstract: Multi-Objective Optimization (MOO) in sequential decision-making tasks is commonly tackled by adapting classical methods (e.g., scalarization, $\varepsilon$-constraints, Pareto-based evolutionary methods) or by training reinforcement learning (RL) models from scratch. In this paper, we propose a different paradigm: orchestrating AI agents to reuse pre-trained RL policies rather than retraining them for each new MOO task. We formalize this setting and develop several algorithmic variants that leverage three core components of agent orchestration: knowledge, tools, and reflection. To demonstrate the practical impact of our approach, we apply it to the Education domain, where our experiments show that agent orchestration achieves a favorable balance between high performance and reduced computational cost.

TL;DR: Agents for multi-objective optimization