データ効率の良い後知恵オフポリシー・オプション学習
Data-efficient Hindsight Off-policy Option Learning
軌跡からオプション選択を推論し動的計画法の推論を逆伝播することで、全ポリシー構成要素をオフポリシーかつエンドツーエンドに学習するHO2を提案し、生ピクセル入力の3Dロボット操作タスクで有効性を示した。
著者: Markus Wulfmeier, Dushyant Rao, Roland Hafner, Thomas Lampe, Abbas Abdolmaleki, Tim Hertweck, Michael Neunert, Dhruva Tirumala, Noah Siegel, Nicolas Heess, Martin Riedmiller
分類: cs.LG, cs.AI, cs.RO, stat.ML
原文アブストラクト
We introduce Hindsight Off-policy Options (HO2), a data-efficient option learning algorithm. Given any trajectory, HO2 infers likely option choices and backpropagates through the dynamic programming inference procedure to robustly train all policy components off-policy and end-to-end. The approach outperforms existing option learning methods on common benchmarks. To better understand the option framework and disentangle benefits from both temporal and action abstraction, we evaluate ablations with flat policies and mixture policies with comparable optimization. The results highlight the importance of both types of abstraction as well as off-policy training and trust-region constraints, particularly in challenging, simulated 3D robot manipulation tasks from raw pixel inputs. Finally, we intuitively adapt the inference step to investigate the effect of increased temporal abstraction on training with pre-trained options and from scratch.
