最適Qガイダンスによるフローマップポリシーの整合
Aligning Flow Map Policies with Optimal Q-Guidance
拡散やフローマッチングに基づく生成ポリシーの推論コストを削減するため、任意のジャンプ（1ステップを含む）を学習するフローマップポリシーを提案し、オフラインからオンラインへの強化学習において、批評家のQ値に基づく信頼領域最適化で適応させる手法を導入した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Christos Ziakas, Alessandra Russo, Avishek Joey Bose
分類: cs.LG
原文アブストラクト
Generative policies based on expressive model classes, such as diffusion and flow matching, are well-suited to complex control problems with highly multimodal action distributions. Their expressivity, however, comes at a significant inference cost: generating each action typically requires simulating many steps of the generative process, compounding latency across sequential decision-making rollouts. We introduce flow map policies, a novel class of generative policies designed for fast action generation by learning to take arbitrary-size jumps including one-step jumps-across the generative dynamics of existing flow-based policies. We instantiate flow map policies for offline-to-online reinforcement learning (RL) and formulate online adaptation as a trust-region optimization problem that improves the critic's Q-value while remaining close to the offline policy. We theoretically derive FLOW MAP Q-GUIDANCE (FMQ), a principled closed-form learning target that is optimal for adapting offline flow map policies under a critic-guided trust-region constraint. We further introduce Q-GUIDED BEAM SEARCH (QGBS), a stochastic flow-map sampler that combines renoising with beam search to enable iterative inference-time refinement. Across 12 challenging robotic manipulation and locomotion tasks from OGBench and RoboMimic, FMQ achieves state-of-the-art performance in offline-to-online RL, outperforming the previous one-step policy MVP by a relative improvement of 21.3% on the average success rate.
