Linking pages
- Anyscale, from the creators of the Ray-distributed computing project, launches with $20.6M led by a16z | TechCrunch https://techcrunch.com/2019/12/17/anyscale-ray-project-distributed-computing-a16z/ 13 comments
- Scaling Multi-Agent Reinforcement Learning – The Berkeley Artificial Intelligence Research Blog https://bair.berkeley.edu/blog/2018/12/12/rllib/ 2 comments
- An Open Source Tool for Scaling Multi-Agent Reinforcement Learning - RISE Lab https://rise.cs.berkeley.edu/blog/scaling-multi-agent-rl-with-rllib/ 1 comment
- Large Scale Training at BAIR with Ray Tune – The Berkeley Artificial Intelligence Research Blog https://bairblog.github.io/2020/01/16/tune/ 0 comments
- Large Scale Training at BAIR with Ray Tune – The Berkeley Artificial Intelligence Research Blog https://bair.berkeley.edu/blog/2020/01/16/tune/ 0 comments
Linked pages
- [1712.05889] Ray: A Distributed Framework for Emerging AI Applications https://arxiv.org/abs/1712.05889 15 comments
- [1703.03864] Evolution Strategies as a Scalable Alternative to Reinforcement Learning https://arxiv.org/abs/1703.03864 12 comments
- GitHub - ray-project/ray: Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads. https://github.com/ray-project/ray 0 comments
- Tune: Scalable Hyperparameter Tuning — Ray 2.3.0 https://ray.readthedocs.io/en/latest/tune.html 0 comments
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