# Selective Experience Sharing in Reinforcement Learning Enhances Interference Management

arXiv: https://arxiv.org/abs/2501.15735 · HTML: https://mlsanity.com/paper/2501.15735/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 27 Jan 2025.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 7 of 7)

- [Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2006.07169/) (2020)
- [Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination](https://mlsanity.com/paper/1907.00123/) (2019)
- [Dynamic Channel Access and Power Control in Wireless Interference Networks via Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2112.14709/) (2021)
- [Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challenges](https://mlsanity.com/paper/2305.07069/) (2023)
- [Sum-Rate Maximization of Multicell MISO Networks with Limited Information Exchange](https://mlsanity.com/paper/1904.05121/) (2019)
- [Cooperative Downlink Interference Transmission and Cancellation for Cellular-Connected UAV: A Divide-and-Conquer Approach](https://mlsanity.com/paper/1906.00220/) (2019)
- [Double-Layer Power Control for Mobile Cell-Free XL-MIMO with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2309.17079/) (2023)

## Abstract (arXiv)

We propose a novel multi-agent reinforcement learning (RL) approach for inter-cell interference mitigation, in which agents selectively share their experiences with other agents. Each base station is equipped with an agent, which receives signal-to-interference-plus-noise ratio from its own associated users. This information is used to evaluate and selectively share experiences with neighboring agents. The idea is that even a few pertinent experiences from other agents can lead to effective learning. This approach enables fully decentralized training and execution, minimizes information sharing between agents and significantly reduces communication overhead, which is typically the burden of interference management. The proposed method outperforms state-of-the-art multi-agent RL techniques where training is done in a decentralized manner. Furthermore, with a 75% reduction in experience sharing, the proposed algorithm achieves 98% of the spectral efficiency obtained by algorithms sharing all experiences.