Experimenting With Reinforcement Learning Using MushroomRL - Episode 332

Summary

Reinforcement learning is a branch of machine learning and AI that has a lot of promise for applications that need to evolve with changes to their inputs. To support the research happening in the field, including applications for robotics, Carlo D’Eramo and Davide Tateo created MushroomRL. In this episode they share how they have designed the project to be easy to work with, so that students can use it in their study, as well as extensible so that it can be used by businesses and industry professionals. They also discuss the strengths of reinforcement learning, how to design problems that can leverage its capabilities, and how to get started with MushroomRL for your own work.

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Announcements

  • Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
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  • Your host as usual is Tobias Macey and today I’m interviewing Davide Tateo and Carlo D’Eramo about MushroomRL, a library for building reinforcement learning experiments

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by describing what reinforcement learning is and how it differs from other approaches for machine learning?
  • What are some example use cases where reinforcement learning might be necessary?
  • Can you describe what MushroomRL is and the story behind it?
    • Who are the target users of the project?
    • What are its main goals?
  • What are your suggestions to other developers for implementing a succesful library?
  • What are some of the core concepts that researchers and/or engineers need to understand to be able to effectively use reinforcement learning techniques?
  • Can you describe how MushroomRL is architected?
    • How have the goals and design of the project changed or evolved since you began working on it?
  • What is the workflow for building and executing an experiment with MushroomRL?
    • How do you track the states and outcomes of experiments?
  • What are some of the considerations involved in designing an environment and reward functions for an agent to interact with?
  • What are some of the open questions that are being explored in reinforcement learning?
  • How are you using MushroomRL in your own research?
  • What are the most interesting, innovative, or unexpected ways that you have seen MushroomRL used?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working on MushroomRL?
  • When is MushroomRL the wrong choice?
  • What do you have planned for the future of MushroomRL?
  • How can the open-source community contribute to MushroomRL?
  • What kind of support you are willing to provide to users?

Keep In Touch

Picks

Closing Announcements

  • Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
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Links

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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