Parental Guidance: Efficient Lifelong Learning through Evolutionary Distillation

2025-03-24

AI Summary

To address weak adaptability, behavioral monotonicity, and poor cross-task generalization in robotic lifelong learning, this paper proposes an evolution-inspired continual adaptation framework. Methodologically, it integrates agent-environment co-evolution, reinforcement learning (RL), and imitation learning, introducing a novel “parent-guided” evolutionary distillation mechanism to enable inheritable representation transfer of task experience; it further designs a co-evolutionary curriculum between agents and terrains to overcome the narrow-domain specialization bottleneck inherent in conventional RL. Contributions include: (i) significantly improved exploration efficiency under sparse rewards and enhanced multi-terrain transfer capability; (ii) spontaneous emergence of diverse locomotion behaviors; and (iii) systematic performance superiority of offspring agents over parents across multiple metrics. The framework establishes a scalable architectural foundation for open-domain lifelong learning.

Technology Category

• Machine Learning: Evolutionary Learning
• Intelligent Robots: Behavior Learning & Control
• Multiagent Systems: Multiagent Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Models for Web evolution
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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.

Problem

• Developing adaptable, diverse robotic agents for complex environments
• Overcoming specialization limits in traditional reinforcement learning methods
• Balancing diversity and specialization via evolutionary-inspired lifelong learning

Innovation

• Evolution-inspired framework balances diversity and specialization
• Integrates RL, IL, and coevolutionary agent-terrain curriculum
• Promotes adaptability, inheritance, and continual learning