Sep 2026
MAdam: MLMC Adam under Markovian Noise with Application to Average-Reward Policy Optimization
Andrey Veprikov, V. Solodkin, A. Chernyavskiy et al.
· Journal of Optimization Theo... · 0 citations
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Decentralized Master-Mind: Joint Action Refinement through Iterative Intent Denoising in Multi-Agent Pathfinding
Results show that round-level intent refinement can improve joint-action coordination while preserving decentralized execution, and DMM, the Decentralized Master-Mind, achieves higher success rates and lower solution costs than the evaluated learnable baselines.
Valeriy Vyaltsev, A. Andreychuk, T. Zlotnikova et al.
· 0 citations