抽象学習モデルを用いた計画と転用可能なサブタスクの学習
Planning with Abstract Learned Models While Learning Transferable Subtasks
サブタスクを記号的に表現する新形式L-AMDPを導入し、複数抽象度で確率的計画に使える遷移・報酬モデルを階層強化学習で獲得するPALMを提案した論文。
著者: John Winder, Stephanie Milani, Matthew Landen, Erebus Oh, Shane Parr, Shawn Squire, Marie desJardins, Cynthia Matuszek
分類: cs.LG, cs.AI, cs.RO, stat.ML
原文アブストラクト
We introduce an algorithm for model-based hierarchical reinforcement learning to acquire self-contained transition and reward models suitable for probabilistic planning at multiple levels of abstraction. We call this framework Planning with Abstract Learned Models (PALM). By representing subtasks symbolically using a new formal structure, the lifted abstract Markov decision process (L-AMDP), PALM learns models that are independent and modular. Through our experiments, we show how PALM integrates planning and execution, facilitating a rapid and efficient learning of abstract, hierarchical models. We also demonstrate the increased potential for learned models to be transferred to new and related tasks.
