マルチタスク方策探索
Multi-Task Policy Search
状態とタスクの両方を入力とするパラメータ化方策を学習し、複数の既知・未知タスクに汎化する単一の方策を実ロボット実験で獲得する手法を提案した。
著者: Marc Peter Deisenroth, Peter Englert, Jan Peters, Dieter Fox
分類: stat.ML, cs.AI, cs.LG, cs.RO
原文アブストラクト
Learning policies that generalize across multiple tasks is an important and challenging research topic in reinforcement learning and robotics. Training individual policies for every single potential task is often impractical, especially for continuous task variations, requiring more principled approaches to share and transfer knowledge among similar tasks. We present a novel approach for learning a nonlinear feedback policy that generalizes across multiple tasks. The key idea is to define a parametrized policy as a function of both the state and the task, which allows learning a single policy that generalizes across multiple known and unknown tasks. Applications of our novel approach to reinforcement and imitation learning in real-robot experiments are shown.
