Ray Deep Dives
What's new in RLlib
Ray Summit 2022
In this 30-min talk, we will go over some of RLlib 2.0's biggest improvements, including:
- A new way of configuring supported algorithms
- Revamped core algorithm logics using plain Python implementations
- Restructured algorithm and policy implementations that are easier to extend and customize
- A feature that consolidates all user environment interactions to make restoring and serving RLlib policies simple and more robust
- Most importantly, the integration between RLlib and the Ray AIR ecosystem
Our goal for all this work is to make RLlib intuitive, easy to extend, and performant for various research and production use cases.
About Jun
Jun Gong is a senior member of the ML team at Anyscale working on RLlib. He is a co-author of a Nature publication on the topic of stratospheric balloon navigation using reinforcement learning. Before Anyscale, he had extensive experience working on distributed and ML systems at Google and Facebook.
About Sven
Sven Mika has been working as a machine learning engineer for Anyscale since early 2020. He is the lead developer of RLlib, Ray's industry-grade, scalable reinforcement learning (RL) library. His team is currently focusing on better supporting the most promising industry use cases, such as massive-multi-agent algorithms for league-based self-play, working with recommender systems and slate recommendation algos such as contextual bandits, and integrating with Ray's new datasets library for a better offline RL experience. A continuing effort of his is asserting high levels of stability and test coverage to ensure RLlib's rapid adoption in industry and research and helping to grow its community and contributor base. Before starting at Anyscale, he has been a leading developer of other successful open-source RL library projects, such as RLgraph and TensorForce.
Jun Gong
Software Engineer, Anyscale
Sven Mika
Machine Learning Engineer, Anyscale
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