Autoencoders have become a hot researched topic in unsupervised learning due to their
ability to learn data features and act as a dimensionality reduction method. With rapid …

Traditional time series prediction methods have shown their outstanding capabilities in time
series prediction. However, due to essential differences in volatility characteristics among …

In recent years, in the field of Multi-Agent Systems (MAS), significant progress has been
made in the research of algorithms that combine Reinforcement Learning (RL) with Attention …

In large-scale unmanned aerial vehicle (UAV) swarm confrontation scenarios, the design of
decision-making and coordination strategies becomes extremely difficult. Multi-Agent …

Large-scale online car-hailing platforms have greatly improved travel efficiency by
dispatching orders quickly. However, effectively scheduling vehicles for a large-scale fleet is …

近年来, 强化学习与注意力机制的结合在算法研究领域备受瞩目. 在强化学习算法中,
注意力机制的应用在提高算法性能方面发挥了重要作用. 重点聚焦于注意力机制在深度强化学习 …

In most existing studies on large-scale multiagent coordination, the control methods aim to
learn discrete policies for agents with finite choices. They rarely consider selecting actions …

Traffic signal control is an important part of intelligent transportation. Efficient traffic signal
control strategies not only alleviate traffic congestion, improve vehicle traffic efficiency, but …

With the proliferation of express delivery services, the demand for intelligent logistic services
is increasing. More and more robots are operating simultaneously, which poses a great …

Reinforcement learning (RL) gained considerable attention by creating decision-making
agents that maximize rewards received from fully observable environments. However, many …