Multi-agent systems require effective coordination between groups and individuals to
achieve common goals. However, current multi-agent reinforcement learning (MARL) …

This work explores the large-scale multi-agent communication mechanism for multi-agent
reinforcement learning (MARL). We summarize the general topology categories for …

We study the problem of inferring communication structures that can solve cooperative multi-
agent planning problems while minimizing the amount of communication. We quantify the …

Many real-world multi-agent systems require agents to cooperate with each other. However,
it is challenging to generate optimal cooperative strategies (eg, location coordination or …

Individuality is essential in human society. It induces the division of labor and thus improves
the efficiency and productivity. Similarly, it should also be a key to multi-agent cooperation …

Most multi-agent reinforcement learning (MARL) approaches optimize strategy by improving
itself, while ignoring the limitations of homogeneous agents that may have single function …

3D object detection from LiDAR point cloud is a challenging task in autonomous driving
systems. Collaborative perception can incorporate information from spatially diverse sensors …

In visual semantic navigation, the robot navigates to a target object with egocentric visual
observations and the class label of the target is given. It is a meaningful task inspiring a …

Deep reinforcement learning has been demonstrated to be an effective solution to the multi-
robot collision avoidance problem. However, with existing methods, robots typically …