most citedA reinforcement learning path planning approach for range-only underwater target localization with autonomous vehicles

8 citations · 8 across the 2 of their papers we have counts for

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cs.LG2023

TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems

Matteo Gallici, Mario Martin, Ivan Masmitja

Coordination is one of the most difficult aspects of multi-agent reinforcement learning (MARL). One reason is that agents normally choose their actions independently of one another…