強化学習のための対照的初期状態バッファ
Contrastive Initial State Buffer for Reinforcement Learning
過去の経験から有益な状態を選んでエージェントの初期状態とし、探索を効率化する手法を提案。四足歩行とドローンレースで性能と収束速度を改善。
著者: Nico Messikommer, Yunlong Song, Davide Scaramuzza
分類: cs.LG
原文アブストラクト
In Reinforcement Learning, the trade-off between exploration and exploitation poses a complex challenge for achieving efficient learning from limited samples. While recent works have been effective in leveraging past experiences for policy updates, they often overlook the potential of reusing past experiences for data collection. Independent of the underlying RL algorithm, we introduce the concept of a Contrastive Initial State Buffer, which strategically selects states from past experiences and uses them to initialize the agent in the environment in order to guide it toward more informative states. We validate our approach on two complex robotic tasks without relying on any prior information about the environment: (i) locomotion of a quadruped robot traversing challenging terrains and (ii) a quadcopter drone racing through a track. The experimental results show that our initial state buffer achieves higher task performance than the nominal baseline while also speeding up training convergence.
