'Reincarnation' in reinforcement learning has been proposed as a formalisation of reusing prior computation from past experiments when training an agent in an environment. In this paper, we present a brief foray into the paradigm of reincarnation in the multi-agent (MA) context. We consider the case where only some agents are reincarnated, whereas the others are trained from scratch -- selective reincarnation. In the fully-cooperative MA setting with heterogeneous agents, we demonstrate that selective reincarnation can lead to higher returns than training fully from scratch, and faster convergence than training with full reincarnation. However, the choice of which agents to reincarnate in a heterogeneous system is vitally important to the outcome of the training -- in fact, a poor choice can lead to considerably worse results than the alternatives. We argue that a rich field of work exists here, and we hope that our effort catalyses further energy in bringing the topic of reincarnation to the multi-agent realm.

翻译：“重生”在强化学习中被提出为一种形式化方法，用于在环境中训练智能体时重用过去实验中的先前计算。本文简要探讨了多智能体（MA）背景下的重生范式。我们考虑仅部分智能体被重生，而其他智能体从零开始训练的情况——即选择性重生。在具有异构智能体的完全协作型多智能体环境中，我们证明选择性重生能够比完全从零训练获得更高回报，且比完全重生训练收敛更快。然而，在异构系统中选择哪些智能体进行重生对训练结果至关重要——事实上，不当的选择可能导致结果远逊于其他方案。我们认为该领域存在丰富的研究空间，并希望我们的工作能推动重生这一主题在多智能体领域引发更多关注。