MAGIC: オンラインPOMDP計画のためのマクロ行動学習
MAGIC: Learning Macro-Actions for Online POMDP Planning
オンラインPOMDP計画の性能をフィードバックとしてマクロ行動生成器をオフラインで学習し、長期的計画の計算効率と性能を向上させる手法を提案した論文。
著者: Yiyuan Lee, Panpan Cai, David Hsu
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
The partially observable Markov decision process (POMDP) is a principled general framework for robot decision making under uncertainty, but POMDP planning suffers from high computational complexity, when long-term planning is required. While temporally-extended macro-actions help to cut down the effective planning horizon and significantly improve computational efficiency, how do we acquire good macro-actions? This paper proposes Macro-Action Generator-Critic (MAGIC), which performs offline learning of macro-actions optimized for online POMDP planning. Specifically, MAGIC learns a macro-action generator end-to-end, using an online planner's performance as the feedback. During online planning, the generator generates on the fly situation-aware macro-actions conditioned on the robot's belief and the environment context. We evaluated MAGIC on several long-horizon planning tasks both in simulation and on a real robot. The experimental results show that the learned macro-actions offer significant benefits in online planning performance, compared with primitive actions and handcrafted macro-actions.
