群体智能体的终身演化
Lifelong Evolution of Swarms
AI总结：
针对群体控制器难跨任务留知识、终身学习少关注群体涌现能力的问题，提出群体终身演化框架，发现种群可保旧任务信息促适应，还设计正则化缓解最优个体的灾难性遗忘，为相关研究提出新问题。
摘要：
适应任务变化且不遗忘已有知识是智能系统的核心能力，也是终身学习的关键维度。然而，群体控制器通常针对特定任务设计，缺乏在任务变化过程中保留知识的能力。另一方面，终身学习研究多聚焦于单个智能体，对群体这类集合体的涌现能力探究有限。为填补这一空白，我们提出一种面向群体的终身演化框架，在动态环境中演化一群群体控制器，环境会逐步呈现新任务。这要求演化过程找到能快速适应新任务、同时保留过往任务知识的控制器，因为旧任务未来可能再次出现。我们发现，种群本身会天然保留过往任务的信息，且能复用这些信息促进适应、缓解遗忘。相比之下，特定任务下表现最优的个体对过往任务存在灾难性遗忘。为缓解这一现象，我们为演化算法设计了正则化流程，降低最优个体的遗忘程度。以终身方式演化群体，对深度终身学习的研究现状以及群体控制器在动态环境中的鲁棒性提出了根本性问题。
英文摘要：
Adapting to task changes without forgetting previous knowledge is a key skill for intelligent systems, and a crucial aspect of lifelong learning. Swarm controllers, however, are typically designed for specific tasks, lacking the ability to retain knowledge across changing tasks. Lifelong learning, on the other hand, focuses on individual agents with limited insights into the emergent abilities of a collective like a swarm. To address this gap, we introduce a lifelong evolutionary framework for swarms, where a population of swarm controllers is evolved in a dynamic environment that incrementally presents novel tasks. This requires evolution to find controllers that quickly adapt to new tasks while retaining knowledge of previous ones, as they may reappear in the future. We discover that the population inherently preserves information about previous tasks, and it can reuse it to foster adaptation and mitigate forgetting. In contrast, the top-performing individual for a given task catastrophically forgets previous tasks. To mitigate this phenomenon, we design a regularization process for the evolutionary algorithm, reducing forgetting in top-performing individuals. Evolving swarms in a lifelong fashion raises fundamental questions on the current state of deep lifelong learning and on the robustness of swarm controllers in dynamic environments.
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