親による指導：進化的蒸留による効率的な生涯学習
Parental Guidance: Efficient Lifelong Learning through Evolutionary Distillation
強化学習・模倣学習・共進化的エージェント地形カリキュラムを組み合わせ、エージェントが複雑なタスクを通じて継続的に進化し、多様性と専門性のバランスを取りながら生涯学習を実現する枠組みを提案した。
著者: Octi Zhang, Quanquan Peng, Rosario Scalise, Bryon Boots
分類: cs.RO, cs.LG, cs.NE
原文アブストラクト
Developing robotic agents that can perform well in diverse environments while showing a variety of behaviors is a key challenge in AI and robotics. Traditional reinforcement learning (RL) methods often create agents that specialize in narrow tasks, limiting their adaptability and diversity. To overcome this, we propose a preliminary, evolution-inspired framework that includes a reproduction module, similar to natural species reproduction, balancing diversity and specialization. By integrating RL, imitation learning (IL), and a coevolutionary agent-terrain curriculum, our system evolves agents continuously through complex tasks. This approach promotes adaptability, inheritance of useful traits, and continual learning. Agents not only refine inherited skills but also surpass their predecessors. Our initial experiments show that this method improves exploration efficiency and supports open-ended learning, offering a scalable solution where sparse reward coupled with diverse terrain environments induces a multi-task setting.
