Reinforcement learning (RL) is gaining traction as a complementary approach to supervised learning for applications from recommender systems to games to production planning. This has been partly due to the ability of new simulators to run code in parallel, thus speeding up training times.
In this training you will learn about RLlib, the most comprehensive open-source RL framework. RLlib is built on top of Ray, an easy-to-use, open-source, distributed computing framework for Python that can handle complex, heterogeneous applications.
Using Colab notebooks, we will combine theoretical concepts of RL with practical exercises. You will leave with a complete, working example of parallelized Python RL code using Ray RLlib on a GitHub repo.
- Learn key deep RL concepts and terminology
- Get an overview of the state-of-the-art RL algorithms available in RLlib
- Learn how to customize an RL environment using OpenAI Gym
- Train and tune an RLlib algorithm (SlateQ and multi-agent PPO) using Ray Tune
- Checkpoint, save, and load the RL model
- Use RL research techniques in offline learning to train the model and evaluate
- Use Python decorators to deploy and serve the trained RL model using Ray Serve
Level: Beginners or intermediate data scientists/ML/RL engineers
Prerequisite knowledge or skills:
- Good skills in Python
- Basics in deep learning in TensorFlow2 or PyTorch
- Read this introduction to RL: https://www.anyscale.com/blog/an-informal-introduction-to-reinforcement-learning
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.
Christy Bergman is a developer advocate at Anyscale. Her work involves figuring out how to parallelize different AI algorithms and creating demos and tutorials on how to use Ray and Anyscale. Before that, she was a senior AI/ML specialist solutions architect at AWS and a data scientist at several other companies. In her spare time, she enjoys hiking and bird watching.
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