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    Hierarchical multi-agent reinforcement learning for cooperative tasks with sparse rewards in continuous domain

    The sparse reward problem has long been one of the most challenging topics in the application of reinforcement learning (RL), especially in complex multi-agent systems. In this paper, a hierarchical multi-agen...

    **gyu Cao, Lu Dong, **n Yuan, Yuanda Wang… in Neural Computing and Applications (2024)

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    Credit assignment in heterogeneous multi-agent reinforcement learning for fully cooperative tasks

    Credit assignment poses a significant challenge in heterogeneous multi-agent reinforcement learning (MARL) when tackling fully cooperative tasks. Existing MARL methods assess the contribution of each agent thr...

    Kun Jiang, Wenzhang Liu, Yuanda Wang, Lu Dong, Changyin Sun in Applied Intelligence (2023)

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    Article

    Multi-objective deep reinforcement learning for crowd-aware robot navigation with dynamic human preference

    The growing development of autonomous systems is driving the application of mobile robots in crowded environments. These scenarios often require robots to satisfy multiple conflicting objectives with different...

    Guangran Cheng, Yuanda Wang, Lu Dong, Wenzhe Cai… in Neural Computing and Applications (2023)