Abstract: 

Ray RLlib implements a wide variety of reinforcement learning algorithms and it provides the tools for adding your own. It integrates with popular frameworks like OpenAI Gym, TensorFlow, and PyTorch. It provides concise abstractions for defining the algorithm and tools you want to use, and specifying the cluster resources available. It is extensible for new algorithms, agents, and environments. Ray does the work to leverage the resources, providing state-of-the-art performance.

Session Outline
This hands-on tutorial teaches you RLlib with the following lessons:
Bipedal Walker: A popular OpenAI Gym environment, used to introduce RLlib concepts.
Optimizing Market Investments with Multi-Armed Bandits. Using bandits with RLlib and different exploration-exploitation strategies.
RL for Recommender Systems: A more advanced example that explores how to customize RLlib for special needs.

Background Knowledge
Basic understanding of Reinforcement Learning Concepts. One may attend the Reinforcement Learning workshop by Leonardo De Marchi before this session.

Bio: 

Known as a "player/coach", with core expertise in data science, natural language, machine learning, cloud computing; 38+ years tech industry experience, ranging from Bell Labs to early-stage start-ups. Advisor for Amplify Partners, IBM Data Science Community, Recognai, KUNGFU.AI, Primer. Lead committer PyTextRank. Formerly: Director, Community Evangelism @ Databricks and Apache Spark. Cited in 2015 as one of the Top 30 People in Big Data and Analytics by Innovation Enterprise.