Many multi-agent navigation approaches make use of simplified representations such as a disk. These simplifications allow for fast simulation of thousands of agents but limit the simulation accuracy and fidelity. In this paper, we propose a fully ...

Coordinating multi-agent reinforcement learning provides a promising approach to scaling learning in large cooperative multi-agent systems. It allows agents to learn local decision policies based on their local observations and rewards, and, meanwhile, ...

If we imagine a dynamic environment whose behavior may change in time we can figure out the difficulties that agents located there will have trying to solve problems related to this environment. Changes in an environment e.g. a market, can be quite ...