Decentralized scheduling through an adaptive, trading-based multi-agent system. (arXiv:2207.11172v1 [cs.AI])

In multi-agent reinforcement learning systems, the actions of one agent can
have a negative impact on the rewards of other agents. One way to combat this
problem is to let agents trade their rewards amongst each other. Motivated by
this, this work applies a trading approach to a simulated scheduling
environment, where the agents are responsible for the assignment of incoming
jobs to compute cores. In this environment, reinforcement learning agents learn
to trade successfully. The agents can trade the …

ai arxiv decentralized scheduling trading