Research map / Multi-agent systems and games / Cooperative multi-agent RL
Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add stochastic recursive variance reduction (GT-SRVR) to GT-GDA | GT-GDA · Decentralized policy evaluation (theory) | sample complexity: O(mnϵ−2) -> O(m√nϵ−2) | Helped |
| replace periodic full gradients with incremental batch size (GT-SRVRI) in GT-SRVR | GT-SRVR · Cooperative Navigation, MSPBE vs sample complexity | convergence speed | Mixed |
| add gradient tracking and variance reduction versus DSGDA / GT-SGDA baselines | GT-SRVR/GT-SRVRI · Cooperative Navigation | communication rounds to convergence | Helped |
Closest papers by meaning
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- Mean-Field Sampling for Cooperative Multi-Agent Reinforcement Learning (NeurIPS 2025 · similarity 0.89)
- Scalable Constrained Policy Optimization for Safe Multi-agent Reinforcement Learning (NeurIPS 2024 · similarity 0.89)
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