VendiRL: 多様なスキルを自己教師あり強化学習で獲得するフレームワーク
VendiRL: A Framework for Self-Supervised Reinforcement Learning of Diversely Diverse Skills
生態学の多様性指標Vendi Scoreを強化学習に導入し、ユーザーが指定した多様性の形でスキルを評価・学習できる統合フレームワークVendiRLを提案した。
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著者: Erik M. Lintunen
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
In self-supervised reinforcement learning (RL), one of the key challenges is learning a diverse set of skills to prepare agents for unknown future tasks. Despite impressive advances, scalability and evaluation remain prevalent issues. Regarding scalability, the search for meaningful skills can be obscured by high-dimensional feature spaces, where relevant features may vary across downstream task domains. For evaluating skill diversity, defining what constitutes "diversity" typically requires a hard commitment to a specific notion of what it means for skills to be diverse, potentially leading to inconsistencies in how skill diversity is understood, making results across different approaches hard to compare, and leaving many forms of diversity unexplored. To address these issues, we adopt a measure of sample diversity that translates ideas from ecology to machine learning -- the Vendi Score -- allowing the user to specify and evaluate any desired form of diversity. We demonstrate how this metric facilitates skill evaluation and introduce VendiRL, a unified framework for learning diversely diverse sets of skills. Given distinct similarity functions, VendiRL motivates distinct forms of diversity, which could support skill-diversity pretraining in new and richly interactive environments where optimising for various forms of diversity may be desirable.
