Scalable cooperative multiagent reinforcement learning in the context of an organization

TL;DRAbstract

Reinforcement learning techniques have been successfully used to solve single agent optimization problems but many of the real problems involve multiple agents, or multi-agent systems. This explains the growing interest in multi-agent reinforcement learning algorithms, or MARL. To be applicable in large real domains, MARL algorithms need to be both stable and scalable. A scalable MARL will be able to perform adequately as the number of agents increases. A MARL algorithm is stable if all agents (eventually) converge to a stable joint policy. Unfortunately, most of the previous approaches lack at least one of these two crucial properties. This dissertation proposes a scalable and stable MARL framework using a network of mediator agents. The network connections restrict the space of valid policies, which reduces the search time and achieves scalability. Optimizing performance in such a system consists of optimizing two subproblems: optimizing mediators' local policies and optimizing the

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