MIMIC-D enables multi-modal multi-agent coordination via joint training of decentralized diffusion policies using only local information.
Monotonic value function factorisation for deep multi- agent reinforcement learning
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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A graph neural network learns to approximate altruistic robot transfers across heterogeneous teams using Hamilton's rule, achieving near-optimal allocation in simulated firefighting scenarios.
A decentralized control framework using time-varying nonsmooth Lyapunov functions lets each agent satisfy only a local constraint, while the sum still converges, including exact decentralized coverage of time-varying densities.
GLo-MAPPO applies centralized-training decentralized-execution MAPPO with a gain-based association scheme to jointly optimize LoRa parameters and UAV paths, yielding higher weighted energy efficiency than prior MARL baselines in simulations.
citing papers explorer
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MIMIC-D: Multi-modal Imitation for MultI-agent Coordination with Decentralized Diffusion Policies
MIMIC-D enables multi-modal multi-agent coordination via joint training of decentralized diffusion policies using only local information.
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Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems
A graph neural network learns to approximate altruistic robot transfers across heterogeneous teams using Hamilton's rule, achieving near-optimal allocation in simulated firefighting scenarios.
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Disentangled Control of Multi-Agent Systems
A decentralized control framework using time-varying nonsmooth Lyapunov functions lets each agent satisfy only a local constraint, while the sum still converges, including exact decentralized coverage of time-varying densities.
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GLo-MAPPO: Multi-Agent Deep Reinforcement Learning for Energy-Efficient UAV-Assisted LoRa Networks
GLo-MAPPO applies centralized-training decentralized-execution MAPPO with a gain-based association scheme to jointly optimize LoRa parameters and UAV paths, yielding higher weighted energy efficiency than prior MARL baselines in simulations.