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.
Counterfactual multi-agent policy gradients
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MADDPG-K scales centralized critics in multi-agent RL by limiting each critic to k-nearest neighbors under Euclidean distance, yielding constant input size and competitive performance.
A multi-agent RL framework for unlabeled multi-robot planning that uses velocity obstacle projections to guarantee collision-free trajectories applicable to arbitrary robot models.
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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Scalable Neighborhood-Based Multi-Agent Actor-Critic
MADDPG-K scales centralized critics in multi-agent RL by limiting each critic to k-nearest neighbors under Euclidean distance, yielding constant input size and competitive performance.
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Learning Safe Unlabeled Multi-Robot Planning with Motion Constraints
A multi-agent RL framework for unlabeled multi-robot planning that uses velocity obstacle projections to guarantee collision-free trajectories applicable to arbitrary robot models.