深層強化学習のためのカリキュラム学習における教師アルゴリズム
Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments
教師アルゴリズムが学習者の能力を把握せずに環境パラメータを逐次サンプリングし、学習進捗を最大化するカリキュラムを自動生成する手法(ALP-GMM)を提案し、BipedalWalkerで評価した。
著者: Rémy Portelas, Cédric Colas, Katja Hofmann, Pierre-Yves Oudeyer
分類: cs.LG, cs.RO, stat.ML
原文アブストラクト
We consider the problem of how a teacher algorithm can enable an unknown Deep Reinforcement Learning (DRL) student to become good at a skill over a wide range of diverse environments. To do so, we study how a teacher algorithm can learn to generate a learning curriculum, whereby it sequentially samples parameters controlling a stochastic procedural generation of environments. Because it does not initially know the capacities of its student, a key challenge for the teacher is to discover which environments are easy, difficult or unlearnable, and in what order to propose them to maximize the efficiency of learning over the learnable ones. To achieve this, this problem is transformed into a surrogate continuous bandit problem where the teacher samples environments in order to maximize absolute learning progress of its student. We present a new algorithm modeling absolute learning progress with Gaussian mixture models (ALP-GMM). We also adapt existing algorithms and provide a complete study in the context of DRL. Using parameterized variants of the BipedalWalker environment, we study their efficiency to personalize a learning curriculum for different learners (embodiments), their robustness to the ratio of learnable/unlearnable environments, and their scalability to non-linear and high-dimensional parameter spaces. Videos and code are available at https://github.com/flowersteam/teachDeepRL.
