分散非同期ガイド付き方策探索による集団ロボット強化学習
Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search
複数ロボットが経験を共有して方策を集団学習する分散非同期版ガイド付き方策探索を提案し、4台のロボットによる視覚ベースのドア開けタスクで単機より優れた汎化・稼働率・学習時間を実証した。
著者: Ali Yahya, Adrian Li, Mrinal Kalakrishnan, Yevgen Chebotar, Sergey Levine
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
In principle, reinforcement learning and policy search methods can enable robots to learn highly complex and general skills that may allow them to function amid the complexity and diversity of the real world. However, training a policy that generalizes well across a wide range of real-world conditions requires far greater quantity and diversity of experience than is practical to collect with a single robot. Fortunately, it is possible for multiple robots to share their experience with one another, and thereby, learn a policy collectively. In this work, we explore distributed and asynchronous policy learning as a means to achieve generalization and improved training times on challenging, real-world manipulation tasks. We propose a distributed and asynchronous version of Guided Policy Search and use it to demonstrate collective policy learning on a vision-based door opening task using four robots. We show that it achieves better generalization, utilization, and training times than the single robot alternative.
