  '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.
