Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …

Real-world problems often exhibit complex relationships and dependencies, which can be
effectively captured by graph learning systems. Graph attention networks (GATs) have …

For discrete-time nonlinear systems, this research is concerned with optimal control
problems (OCPs) with constrained cost, and a novel value iteration with constrained cost …

Multi-agent reinforcement learning is promising for real-world applications as it encourages
agents to perceive and interact with their surrounding environment autonomously. However …

The automated guided vehicle (AGV) dispatching problem is to develop a rule to assign
transportation tasks to certain vehicles. This article proposes a new deep reinforcement …

Aiming at the six-rotor unmanned aerial vehicles subjected to false data injection attacks, an
event-triggered-based distributed intelligent learning control strategy is proposed in this …

Multimodal synchronization has become the research highlight of the ITS, where complex
driving scenarios, various types of vehicles and diverse data sources are crucial …

State-of-the-art (SOTA) multiagent reinforcement algorithms distinguish themselves in many
ways from their single-agent equivalences. However, most of them still totally inherit the …

In the multi-agent cooperative decision-making process, an agent needs to learn
cooperatively with its neighbors to obtain the optimal strategy. The actions of agents can be …

Existing multi-agent reinforcement learning methods employ a paradigm of centralized
training with decentralized execution (CTDE) to learn cooperative policy among agents via …