智能体选择性共享经验，大幅降低通信开销同时提升干扰管理效果。
Selective Experience Sharing in Reinforcement Learning Enhances Interference Management
- 只共享关键经验，实现去中心化训练与执行。
- 通信量减少75%仍保持98%的频谱效率。
- 适合大规模无线网络中低延迟干扰协调场景。
我们提出一种新型多智能体强化学习方法，用于小区间干扰抑制。每个基站配备一个智能体，接收自身关联用户信号与干扰加噪声比（SINR）信息，据此评估并选择性地与其他邻近智能体共享经验。核心思想是：少量相关经验即可实现高效学习。该方法支持完全去中心化训练与部署，显著减少智能体间的信息交换，大幅降低干扰管理中的通信开销。实验表明，该方法在分布式训练中优于现有先进多智能体强化学习技术。尤其值得注意的是，在经验共享减少75%的情况下，仍可达到完全共享时98%的频谱效率。
原文摘要 · Abstract (English)
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.
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