Selective Experience Sharing in Reinforcement Learning Enhances Interference Management
On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization
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.
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.
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7 of 20 references.
References · 7
- 2020Shared Experience Actor-Critic for Multi-Agent Reinforcement Learningcited by 116
- 2019Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordinationcited by 18
- 2021Dynamic Channel Access and Power Control in Wireless Interference Networks via Multi-Agent Deep Reinforcement Learningcited by 5
- 2023Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challengescited by 3
- 2019Sum-Rate Maximization of Multicell MISO Networks with Limited Information Exchangecited by 2
- 2019Cooperative Downlink Interference Transmission and Cancellation for Cellular-Connected UAV: A Divide-and-Conquer Approachcited by 1
- 2023Double-Layer Power Control for Mobile Cell-Free XL-MIMO with Multi-Agent Reinforcement Learningcited by 1
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- 2023DIFFER: Decomposing Individual Reward for Fair Experience Replay in Multi-Agent Reinforcement Learning ↗
- 2025Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning
- 2024SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks
- 2024Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs