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CESMA: Centralized Expert Supervises Multi-Agents. (arXiv:1902.02311v2 [cs.MA] UPDATED)


We consider the reinforcement learning problem of training multiple agents in
order to maximize a shared reward. In this multi-agent system, each agent seeks
to maximize the reward while interacting with other agents, and they may or may
not be able to communicate. Typically the agents do not have access to other
agent policies and thus each agent observes a non-stationary and
partially-observable environment. In order to resolve this issue, we
demonstrate a novel multi-agent training framework that first turns a
multi-agent problem into a single-agent problem to obtain a centralized expert
that is then used to guide supervised learning for multiple independent agents
with the goal of decentralizing the policy. We additionally demonstrate a way
to turn the exponential growth in the joint action space into a linear growth
for the centralized policy. Overall, the problem is twofold: the problem of
obtaining a centralized expert, and then the problem of supervised learning to
train the multi-agents. We demonstrate our solutions to both of these tasks,
and show that supervised learning can be used to decentralize a multi-agent
policy.

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