軌道最適化と方策学習の合意形成による高精度ロボット制御
Enforcing the consensus between Trajectory Optimization and Policy Learning for precise robot control
軌道最適化の感度情報をSobolev学習で活用し、拡張ラグランジュ法で方策学習との一致を促すことで、ロボット制御のグローバル方策をより速く学習する手法を提案した。
著者: Quentin Le Lidec, Wilson Jallet, Ivan Laptev, Cordelia Schmid, Justin Carpentier
分類: cs.RO, cs.LG
原文アブストラクト
Reinforcement learning (RL) and trajectory optimization (TO) present strong complementary advantages. On one hand, RL approaches are able to learn global control policies directly from data, but generally require large sample sizes to properly converge towards feasible policies. On the other hand, TO methods are able to exploit gradient-based information extracted from simulators to quickly converge towards a locally optimal control trajectory which is only valid within the vicinity of the solution. Over the past decade, several approaches have aimed to adequately combine the two classes of methods in order to obtain the best of both worlds. Following on from this line of research, we propose several improvements on top of these approaches to learn global control policies quicker, notably by leveraging sensitivity information stemming from TO methods via Sobolev learning, and augmented Lagrangian techniques to enforce the consensus between TO and policy learning. We evaluate the benefits of these improvements on various classical tasks in robotics through comparison with existing approaches in the literature.
