混合可観測性下での階層型強化学習
Hierarchical Reinforcement Learning under Mixed Observability
一部の状態変数だけが観測可能なMOMDPの部分クラスに対し、上位が目標を生成し下位がそれを達成する2層階層型RL手法HILMOを提案し、長期的連続制御タスクで成功率・サンプル効率・学習時間を改善、実機にも展開した。
著者: Hai Nguyen, Zhihan Yang, Andrea Baisero, Xiao Ma, Robert Platt, Christopher Amato
分類: cs.RO
原文アブストラクト
The framework of mixed observable Markov decision processes (MOMDP) models many robotic domains in which some state variables are fully observable while others are not. In this work, we identify a significant subclass of MOMDPs defined by how actions influence the fully observable components of the state and how those, in turn, influence the partially observable components and the rewards. This unique property allows for a two-level hierarchical approach we call HIerarchical Reinforcement Learning under Mixed Observability (HILMO), which restricts partial observability to the top level while the bottom level remains fully observable, enabling higher learning efficiency. The top level produces desired goals to be reached by the bottom level until the task is solved. We further develop theoretical guarantees to show that our approach can achieve optimal and quasi-optimal behavior under mild assumptions. Empirical results on long-horizon continuous control tasks demonstrate the efficacy and efficiency of our approach in terms of improved success rate, sample efficiency, and wall-clock training time. We also deploy policies learned in simulation on a real robot.
