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About this paper
Bahman Abolhassani, Tugba Erpek, Kemal Davaslioglu, Yalin E. Sagduyu, Sastry Kompella
Researchers from Nexcepta developed a Multi-Agent Reinforcement Learning (MARL) framework using QMIX to enhance communication resilience in robotic swarm networks against reactive jammers. The framework achieves near-optimal anti-jamming performance in simplified scenarios and significantly outperforms non-learning baselines in complex environments with channel reuse and fading.
Key insights
- The QMIX framework successfully learns cooperative, observation-driven policies for channel and power allocation among swarm agents, even under dynamic reactive jamming.
- Centralized Training, Decentralized Execution (CTDE) effectively enables robust, distributed operation where agents act solely on local observations after training, maintaining performance consistency.
- Carefully designed reward functions, incorporating distance-aware interference penalties, are crucial for encouraging efficient channel reuse and suppressing harmful co-channel clustering in multi-agent environments.
Summary by alphaXiv.
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