適応的行動スケーリングによる制約認識型強化学習
Constraint-Aware Reinforcement Learning via Adaptive Action Scaling
予測される制約違反に応じてエージェントの行動を滑らかにスケーリングするモジュール型調整器を提案し、SACやTD3と組み合わせて安全性と性能を両立させた。
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: Murad Dawood, Usama Ahmed Siddiquie, Shahram Khorshidi, Maren Bennewitz
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
Safe reinforcement learning (RL) seeks to mitigate unsafe behaviors that arise from exploration during training by reducing constraint violations while maintaining task performance. Existing approaches typically rely on a single policy to jointly optimize reward and safety, which can cause instability due to conflicting objectives, or they use external safety filters that override actions and require prior system knowledge. In this paper, we propose a modular cost-aware regulator that scales the agent's actions based on predicted constraint violations, preserving exploration through smooth action modulation rather than overriding the policy. The regulator is trained to minimize constraint violations while avoiding degenerate suppression of actions. Our approach integrates seamlessly with off-policy RL methods such as SAC and TD3, and achieves state-of-the-art return-to-cost ratios on Safety Gym locomotion tasks with sparse costs, reducing constraint violations by up to 126 times while increasing returns by over an order of magnitude compared to prior methods.
