Cooperative multi-agent reinforcement learning (MARL) has made prominent progress in
recent years. For training efficiency and scalability, most of the MARL algorithms make all …

In many real-world tasks, a team of agents learn to cooperate with each other under the
setting of partial observability and communication constraints, where value factorization has …

Due to the partial observability and communication constraints in many multiagent reinforcement
learning (MARL) tasks, centralized training with decentralized execution (CTDE) has …

Recent years have witnessed the great breakthrough of deep reinforcement learning (DRL)
in various perfect and imperfect information games. Among these games, DouDizhu, a …

Historical interactions are the default choice for recommender model training, which typically
exhibit high sparsity, ie, most user-item pairs are unobserved missing data. A standard …

With the development of deep reinforcement learning, much progress in various perfect and
imperfect information games has been achieved. Among these games, DouDizhu, a popular …

Multi-agent reinforcement learning is difficult to apply in practice, partially because of the
gap between simulated and real-world scenarios. One reason for the gap is that simulated …

The quest for novel deformable image sensors with outstanding optoelectronic properties
and large‐scale integration becomes a great impetus to exploit more advanced flexible …

A strain modulated solar‐blinded photodetector (PD) based on ZnO‐Ga 2 O 3 core–shell
heterojuction microwire is developed. This PD is highly sensitive to deep UV light centered at …

African swine fever, caused by African swine fever virus (ASFV), is a highly contagious
hemorrhagic disease of domestic pigs. The current continent-wide pandemic has persisted for …