A long-standing challenge in distributed wireless systems is ensuring efficient and fair random channel access. Existing solutions often address specific constraints related to timing, periodicity, or centralization, but they typically rely on fixed heuristics. Motivated by recent advances in machine learning (ML), we investigate whether ML agents can autonomously learn efficient and fair access strategies, and whether such learning can offer new insights into medium access control (MAC) design. Rather than proposing a deployable protocol, our aim is to examine whether decentralized learning can rediscover or approximate theoretically efficient random-access mechanisms under minimal assumptions. To this end, we deploy an off-policy Double Deep Q-Network (DDQN) with Bayesian inference to train agents operating over a slotted channel. The resulting method is fully online (no pre-training), fully distributed (independent multi-agent learners), stochastic (non-periodic), and requires no coordination or explicit communication. Extensive simulations show that the learned strategy adapts to varying network conditions and achieves near-theoretical efficiency while maintaining fairness. Ablation studies further reveal that the learned behavior resembles slotted ALOHA with a dynamically adjusted transmission probability, leading us to refer to the method as KISS: Keeping It Simple and Slotted.

翻译：分布式无线系统中一个长期存在的挑战是确保高效且公平的随机信道接入。现有解决方案通常针对时序、周期性或集中化等特定约束进行设计，但往往依赖于固定启发式方法。受近期机器学习进展的启发，我们研究了智能体能否自主学习高效且公平的接入策略，以及此类学习能否为介质访问控制设计提供新的见解。本文的目标并非提出可部署的协议，而是探究在最小假设条件下，去中心化学习能否重新发现或近似逼近理论上高效的随机接入机制。为此，我们部署了结合贝叶斯推理的离策略双深度Q网络来训练在时隙化信道上运行的智能体。所提方法完全在线运行（无需预训练）、完全分布式（独立多智能体学习器）、随机化（非周期性），且无需协调或显式通信。大量仿真表明，学习到的策略能适应变化的网络条件，在保持公平性的同时达到接近理论效率的性能。消融研究进一步揭示，学习到的行为类似于具有动态调整传输概率的时隙ALOHA，因此我们将该方法称为KISS：保持简单与时隙化。