- You are subscribed to this talk through .
- You are watching this talk through .
- You are subscribed to this talk. (unsubscribe, watch)
- You are watching this talk. (unwatch, subscribe)
- You are not subscribed to this talk. (watch, subscribe)
We present JAX-based libraries and tools to support an ecosystem for Multi-Agent Reinforcement Learning research. Our ecosystem contributions include stable and performant software, tightly integrated with scalable yet hackable online and offline algorithms with environments, datasets, and evaluation protocols to produce reliable research artefacts. Developed by researchers for researchers, our stack should allow anyone in the MARL community to go from proposal to paper with confidence.
The MARL research team at InstaDeep works on large-scale multi-agent learning with a focus on algorithmic innovation in cooperative systems for industrial applications. The team regularly contributes to the research community through publications at venues such as NeurIPS and ICLR, and to the open-source community, by releasing open-source software for MARL research.
Note: Please register using the Google Form on our website https://go.umd.edu/marl for access to the Google Meet, Open-source Multi-Agent AI Research Community and talk resources.