スキル事前分布を学習して強化学習を加速する
Accelerating Reinforcement Learning with Learned Skill Priors
オフライン経験からスキルの埋め込み空間とスキル事前分布を同時に学習し、下流の強化学習でその事前分布を活用して効率的なスキル転移を実現した研究。
著者: Karl Pertsch, Youngwoon Lee, Joseph J. Lim
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
Intelligent agents rely heavily on prior experience when learning a new task, yet most modern reinforcement learning (RL) approaches learn every task from scratch. One approach for leveraging prior knowledge is to transfer skills learned on prior tasks to the new task. However, as the amount of prior experience increases, the number of transferable skills grows too, making it challenging to explore the full set of available skills during downstream learning. Yet, intuitively, not all skills should be explored with equal probability; for example information about the current state can hint which skills are promising to explore. In this work, we propose to implement this intuition by learning a prior over skills. We propose a deep latent variable model that jointly learns an embedding space of skills and the skill prior from offline agent experience. We then extend common maximum-entropy RL approaches to use skill priors to guide downstream learning. We validate our approach, SPiRL (Skill-Prior RL), on complex navigation and robotic manipulation tasks and show that learned skill priors are essential for effective skill transfer from rich datasets. Videos and code are available at https://clvrai.com/spirl.
