タスク非依存スキルの深層強化学習による合成
Composing Task-Agnostic Policies with Deep Reinforcement Learning
深層強化学習で学習した基本スキルを組み合わせ、未知のタスクを少ないデータで解くスキル転移・合成手法を提案した論文。
著者: Ahmed H. Qureshi, Jacob J. Johnson, Yuzhe Qin, Taylor Henderson, Byron Boots, Michael C. Yip
分類: cs.LG, cs.AI, cs.RO, stat.ML
原文アブストラクト
The composition of elementary behaviors to solve challenging transfer learning problems is one of the key elements in building intelligent machines. To date, there has been plenty of work on learning task-specific policies or skills but almost no focus on composing necessary, task-agnostic skills to find a solution to new problems. In this paper, we propose a novel deep reinforcement learning-based skill transfer and composition method that takes the agent's primitive policies to solve unseen tasks. We evaluate our method in difficult cases where training policy through standard reinforcement learning (RL) or even hierarchical RL is either not feasible or exhibits high sample complexity. We show that our method not only transfers skills to new problem settings but also solves the challenging environments requiring both task planning and motion control with high data efficiency.
