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The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts.

, , , , , and . AAMAS, page 1804-1808. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), (2022)

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Modeling Partially Observable Systems using Graph-Based Memory and Topological Priors., , , , and . L4DC, volume 168 of Proceedings of Machine Learning Research, page 59-73. PMLR, (2022)ModGNN: Expert Policy Approximation in Multi-Agent Systems with a Modular Graph Neural Network Architecture., and . ICRA, page 9161-9167. IEEE, (2021)The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts., , , , , and . AAMAS, page 1804-1808. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), (2022)VMAS: A Vectorized Multi-agent Simulator for Collective Robot Learning., , , and . DARS, volume 28 of Springer Proceedings in Advanced Robotics, page 42-56. Springer, (2022)The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts., , , , , and . CoRR, (2021)Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning., , and . CoRR, (2024)Revisiting Recurrent Reinforcement Learning with Memory Monoids., , , , , and . CoRR, (2024)Permutation-Invariant Set Autoencoders with Fixed-Size Embeddings for Multi-Agent Learning., , and . AAMAS, page 1661-1669. ACM, (2023)Reinforcement Learning with Fast and Forgetful Memory., , , and . CoRR, (2023)POPGym: Benchmarking Partially Observable Reinforcement Learning., , , , and . ICLR, OpenReview.net, (2023)