A beginner’s tutorial for working with multi-agent environments, models, and algorithms.
“Hands-on RL with Ray’s RLlib” is a beginners tutorial for working with reinforcement learning (RL) multi-agent environments, models, and algorithms using Ray’s RLlib library. RLlib offers high scalability, a large list of algorithms to choose from (offline, model-based, model-free, etc..), support for TensorFlow and PyTorch, and a unified API for a variety of applications and customizations. This tutorial includes a brief introduction to provide an overview of concepts (e.g. why RL) before proceeding to RLlib models, hyperparameter tuning, debugging, student exercises, Q/A, and more. All code will be provided as .py files in a GitHub repo.
Python programmers who want to get started with reinforcement learning and RLlib
Some Python programming experience
Some familiarity with machine learning
Experience in reinforcement learning and Ray would be helpful, but isn’t required
Experience with TensorFlow or PyTorch would be helpful, but isn’t required
What is reinforcement learning and why RLlib
How to configure and hyperparameter tune RLlib
RLlib debugging best practices
Sven is a machine learning engineer at Anyscale Inc. and the lead developer of RLlib, Ray's industry-leading, open-source reinforcement learning (RL) library. He is currently focusing his efforts on making offline RL algorithms a viable solution for many industry use cases, making attention nets the new go-to models for partially observable environments, designing new APIs and simplifying existing ones, as well as, asserting high levels of stability and test coverage to ensure RLlib's rapid adoption in industry and research. He is also actively helping to grow the community and contributor base around RLlib. Before starting at Anyscale, he has been a leading developer of other successful open-source RL library projects, such as "RLgraph" and "TensorForce".