Research map / Multi-agent systems and games / Cooperative multi-agent RL
Efficient Multi-agent Communication via Self-supervised Information Aggregation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add MASIA message aggregation to VDN, QMIX and QPLEX | value-decomposition baselines · SMAC 1o10b_vs_1r | average test win rate | Helped |
| narrow agents' sight range from 9 to 6 and 3 (ablation) | MASIA · SMAC 1o10b_vs_1r | median test win rate | No clear effect |
| replace learned messages with MASIA aggregation over no communication (QMIX) | QMIX · Hallway | median test win rate | Helped |
| use targeted or minimized messages (TarMAC, NDQ, TMC) instead of aggregation | communication baselines · Hallway | median test win rate | Hurt |
| add MASIA communication on top of QMIX | QMIX · LBF | median test win rate | No clear effect |
Closest papers by meaning
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- Learning Efficient and Robust Multi-Agent Communication via Graph Information Bottleneck (AAAI 2024 · similarity 0.89)
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- Multi-Agent Reinforcement Learning with Communication-Constrained Priors (NeurIPS 2025 · similarity 0.88)
- PMAC: Personalized Multi-Agent Communication (AAAI 2024 · similarity 0.88)
- Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning (ICLR 2025 · similarity 0.87)
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