タスク非依存の行動空間を学習する移動最適化手法
Learning Task-Agnostic Action Spaces for Movement Optimization
物理ベースのキャラクター制御において、目標状態を行動として学習する低レベル方策を参照データなしで獲得し、多様なタスクの移動最適化を効率化する手法を提案した論文。
著者: Amin Babadi, Michiel van de Panne, C. Karen Liu, Perttu Hämäläinen
分類: cs.LG, cs.RO, cs.SY, eess.SY, stat.ML
原文アブストラクト
We propose a novel method for exploring the dynamics of physically based animated characters, and learning a task-agnostic action space that makes movement optimization easier. Like several previous papers, we parameterize actions as target states, and learn a short-horizon goal-conditioned low-level control policy that drives the agent's state towards the targets. Our novel contribution is that with our exploration data, we are able to learn the low-level policy in a generic manner and without any reference movement data. Trained once for each agent or simulation environment, the policy improves the efficiency of optimizing both trajectories and high-level policies across multiple tasks and optimization algorithms. We also contribute novel visualizations that show how using target states as actions makes optimized trajectories more robust to disturbances; this manifests as wider optima that are easy to find. Due to its simplicity and generality, our proposed approach should provide a building block that can improve a large variety of movement optimization methods and applications.
