役割駆動型報酬進化フレームワークR²E：自動報酬関数設計
$R^2E:$ A Role-driven Reward Evolutionary Framework for Automated Reward Function Design
LLMの複数役割（探索・保守・進化）と動的スケジューリング、エリートプールで報酬関数を自動設計し、ロボットタスクで有効性を示した論文。
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著者: Shouhao Chang, Xuan Liu, Hongye Zhu, Xinning Chen, Shigeng Zhang
分類: cs.RO
原文アブストラクト
Designing effective reward functions is a critical challenge in Reinforcement Learning (RL), which traditionally requires costly manual trial-and-error. While Large Language Model (LLM)-based methods have shown promise in reward design, undifferentiated sampling and limited use of historical feedback often lead to insufficient exploration of the reward space and optimization instability. To address these challenges, we propose $R^2E$, a Role-driven Reward Evolutionary framework for automated reward function design that structures the process through explicit role specialization. $R^2E$ employs multiple LLM roles with complementary objectives: an Explorer that promotes novelty to expand the search space, a Guardian that performs conservative refinements to improve training stability, and an Evolver that recombines high-performing rewards via crossover and mutation to integrate effective structures. A dynamic scheduling strategy coordinates these roles, progressively shifting the search from broad exploration to focused exploitation. To mitigate optimization instability and provide a medium for cross-role collaboration, $R^2E$ incorporates a Global Elite Pool that retains the best-performing rewards to guide subsequent generations, thereby enabling coordinated interaction among roles and ensuring a reliable and consistent refinement process. Extensive experiments on multiple robotic tasks demonstrate the effectiveness of our method.