Multi-agents systems (MAS) are a core area of research in artificial intelligence. Such systems consist of several decision-making agents which interact in a shared environment. Using MAS a wide range of applications can be addressed including autonomous driving, the semantic web, smart grids, multi-robot factories, automated trading, etc.
On this page you can find an overview of tools and open source libraries for developing and comparing MAS algorithms.
On the one hand, the toolbox is intended to be a visualization and simulation instrument in which we document state-of-the art methods and provide guidelines on when to use which approach. It currently contains two prototypical environments and several of their subproblems.
On the other hand, it also contains an overview of our open source libraries for developing and comparing MAS algorithms. In particular we have developed MOMAland, the first multi-objective multi-agent reinforcement learning library which is part of the Farama Foundation, a non-profit organisation that maintains open source RL tools.
You can send questions and comments to marjon.blondeel@vub.be.
