Introducing RLlib: A Composable and Scalable Reinforcement Learning Library

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In a previous post, I outlined emerging applications of reinforcement learning (RL) in industry. I began by listing a few challenges facing anyone wanting to apply RL, including the need for large amounts of data, and the difficulty of reproducing research results and deriving the error estimates needed for mission-critical applications. Nevertheless, the success of RL in certain domains has been the subject of much media coverage. This has sparked interest, and companies are beginning to explore some of the use cases and applications I described in my earlier post. Many tasks and professions, including software development, are poised to incorporate some forms of AI-powered automation. In this post, I’ll describe how RISE Lab’s Ray platform continues to mature and evolve just as companies are examining use cases for RL.

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