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.


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Training Overview

  • 1

    ODSC West 2020: Hands-on Reinforcement Learning with Ray RLlib

    • Training Overview and Author Bio

    • Before you get started: Prerequisites and Resources

    • Hands-on Reinforcement Learning with Ray RLlib

Instructor Bio:

Paco Nathan

Data Scientist | Derwen, Inc.

Paco Nathan

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.