# Dynamic Multichannel Access via Multi-agent Reinforcement Learning: Throughput and Fairness Guarantees

arXiv: https://arxiv.org/abs/2105.04077 · HTML: https://mlsanity.com/paper/2105.04077/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 50 references that are in the map.
- First posted on arXiv 10 May 2021.

## Builds on (most cited first, 12 of 12)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Action Branching Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1711.08946/) (2017)
- [Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks](https://mlsanity.com/paper/1808.00490/) (2018)
- [Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks](https://mlsanity.com/paper/1712.00162/) (2017)
- [Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks](https://mlsanity.com/paper/1802.06958/) (2018)
- [Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach](https://mlsanity.com/paper/1810.11758/) (2018)
- [Distributed Learning for Channel Allocation Over a Shared Spectrum](https://mlsanity.com/paper/1902.06353/) (2019)
- [A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access](https://mlsanity.com/paper/1908.08401/) (2019)
- [Spectrum Access In Cognitive Radio Using A Two Stage Reinforcement Learning Approach](https://mlsanity.com/paper/1707.09792/) (2017)
- [Random Access Protocols for Massive MIMO](https://mlsanity.com/paper/1606.02080/) (2016)

## Built on by (most cited first, 4 of 4)

- [A Semantic-Aware Multiple Access Scheme for Distributed, Dynamic 6G-Based Applications](https://mlsanity.com/paper/2401.06308/) (2024)
- [REFINE-LM: Mitigating Language Model Stereotypes via Reinforcement Learning](https://mlsanity.com/paper/2408.09489/) (2024)
- [Multi-Agent Conditional Diffusion Model with Mean Field Communication as Wireless Resource Allocation Planner](https://mlsanity.com/paper/2510.22969/) (2025)
- [Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2503.24296/) (2025)

## Abstract (arXiv)

We consider a multichannel random access system in which each user accesses a single channel at each time slot to communicate with an access point (AP). Users arrive to the system at random and be activated for a certain period of time slots and then disappear from the system. Under such dynamic network environment, we propose a distributed multichannel access protocol based on multi-agent reinforcement learning (RL) to improve both throughput and fairness between active users. Unlike the previous approaches adjusting channel access probabilities at each time slot, the proposed RL algorithm deterministically selects a set of channel access policies for several consecutive time slots. To effectively reduce the complexity of the proposed RL algorithm, we adopt a branching dueling Q-network architecture and propose an efficient training methodology for producing proper Q-values over time-varying user sets. We perform extensive simulations on realistic traffic environments and demonstrate that the proposed online learning improves both throughput and fairness compared to the conventional RL approaches and centralized scheduling policies.