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This paper proposes a multi-agent reinforcement learning based medium accessframework for wireless networks. The access problem is formulated as a MarkovDecision Process (MDP), and solved using reinforcement learning with everynetwork node acting as a distributed learning agent. The solution componentsare developed step by step, starting from a single-node access scenario inwhich a node agent incrementally learns to control MAC layer packet loads forreining in self-collisions. The strategy is then scaled up for multi-nodefully-connected scenarios by using more elaborate reward structures. It alsodemonstrates preliminary feasibility for more general partially connectedtopologies. It is shown that by learning to adjust MAC layer transmissionprobabilities, the protocol is not only able to attain theoretical maximumthroughput at an optimal load, but unlike classical approaches, it can alsoretain that maximum throughput at higher loading conditions. Additionally, themechanism is agnostic to heterogeneous loading while preserving that feature.It is also shown that access priorities of the protocol across nodes can beparametrically adjusted. Finally, it is also shown that the online learningfeature of reinforcement learning is able to make the protocol adapt totime-varying loading conditions.

Towards Multi-agent Reinforcement Learning for Wireless Network Protocol Synthesis