TRAINING: Introduction to reinforcement learning and RLlib

Monday, August 22
1:00 PM - 4:30 PM

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.

This course will teach you how to create a multiplayer game, solvable by RL, using RLlib. You will learn the basics of RL and how to use RLlib to train the different behaviors of the game's characters. We will then optimize our models' hyperparameters with Ray Tune and also learn how to add an in-game item recommendation system using offline RL.

Using Jupyter notebooks running on Anyscale in the cloud, 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.

Key takeaways:

  • 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:

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.

About Christy

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.

Sven Mika

Machine Learning Engineer, Anyscale

Christy Bergman

Developer Advocate, Anyscale
Ray Summit 2022 horizontal logo

Ready to Register?

Come connect with the global community of thinkers and disruptors who are building and deploying the next generation of AI and ML applications.

Save your spot

Join the Conversation

Ready to get involved in the Ray community before the conference? Ask a question in the forums. Open a pull request. Or share why you’re excited with the hashtag #RaySummit on Twitter.