This paper proposes a multi-agent reinforcement learning based medium access
framework for wireless networks. The access problem is formulated as a Markov
Decision Process (MDP), and solved using reinforcement learning with every
network node acting as a distributed learning agent. The solution components
are developed step by step, starting from a single-node access scenario in
which a node agent incrementally learns to control MAC layer packet loads for
reining in self-collisions. The strategy is then scaled up for multi-node
fully-connected scenarios by using more elaborate reward structures. It also
demonstrates preliminary feasibility for more general partially connected
topologies. It is shown that by learning to adjust MAC layer transmission
probabilities, the protocol is not only able to attain theoretical maximum
throughput at an optimal load, but unlike classical approaches, it can also
retain that maximum throughput at higher loading conditions. Additionally, the
mechanism is agnostic to heterogeneous loading while preserving that feature.
It is also shown that access priorities of the protocol across nodes can be
parametrically adjusted. Finally, it is also shown that the online learning
feature of reinforcement learning is able to make the protocol adapt to
time-varying loading conditions.

本文提出了一个基于多智能体强化学习的中介访问框架，使用强化学习解决无线网络中的访问问题，通过调整 MAC 层传输概率来实现最优负载并适应时间变化的负载，同时保持对异构负载的不可知性。

面向无线网络协议合成的多智能体强化学习

Towards Multi-agent Reinforcement Learning for Wireless Network Protocol  Synthesis

Network management tools are usually inherited from one generation to
another. This was successful since these tools have been kept in check and
updated regularly to fit new networking goals and service requirements.
Unfortunately, new networking services will render this approach obsolete and
handcrafting new tools or upgrading the current ones may lead to complicated
systems that will be extremely difficult to maintain and improve. Fortunately,
recent advances in AI have provided new promising tools that can help solving
many network management problems. Following this interesting trend, the current
article presents LEASCH, a deep reinforcement learning model able to solve the
radio resource scheduling problem in the MAC layer of 5G networks. LEASCH is
developed and trained in a sand-box and then deployed in a 5G network. The
experimental results validate the effectiveness of LEASCH compared to
conventional baseline methods in many key performance indicators.

本文介绍了 LEASCH，一款基于深度强化学习的模型，可在 5G 网络的 MAC 层中解决无线电资源调度问题，实验证明其优于传统基准方法，解决了新一代网络服务带来的网络管理挑战。