PE-MAMoE combines sparsely gated mixture-of-experts actors with a non-parametric phase controller in MAPPO to maintain plasticity under dynamic user mobility and traffic, yielding 26.3% higher normalized IQM return in simulations.
A survey of multi-agent reinforcement learning with communi- cation
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AsynCoMARL is a new asynchronous MARL algorithm that matches leading baselines on success and collision rates while using 26% fewer messages via graph transformers on dynamic communication graphs.
SwarmHarness is a proposed decentralized protocol for compute sharing among AI agents via DHT registry, load-aware routing, and credit incentives that penalize non-contributors.
citing papers explorer
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Plasticity-Enhanced Multi-Agent Mixture of Experts for Dynamic Objective Adaptation in UAVs-Assisted Emergency Communication Networks
PE-MAMoE combines sparsely gated mixture-of-experts actors with a non-parametric phase controller in MAPPO to maintain plasticity under dynamic user mobility and traffic, yielding 26.3% higher normalized IQM return in simulations.
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Asynchronous Cooperative Multi-Agent Reinforcement Learning with Limited Communication
AsynCoMARL is a new asynchronous MARL algorithm that matches leading baselines on success and collision rates while using 26% fewer messages via graph transformers on dynamic communication graphs.
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SwarmHarness: Skill-Based Task Routing via Decentralized Incentive-Aligned AI Agent Networks
SwarmHarness is a proposed decentralized protocol for compute sharing among AI agents via DHT registry, load-aware routing, and credit incentives that penalize non-contributors.