Parental Guidance: Efficient Lifelong Learning through Evolutionary Distillation
This work addresses the problem of limited adaptability and diversity in robotic agents for AI and robotics, though it appears incremental as it builds on existing methods like RL and IL.
The paper tackles the challenge of creating adaptable and diverse robotic agents by proposing an evolution-inspired framework that integrates reinforcement learning, imitation learning, and a coevolutionary curriculum. Initial experiments show improved exploration efficiency and support for open-ended learning in multi-task settings.
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.