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Episode 43: Deep Reinforcement Learning in a Notebook With Jupylet + Gaming and Synthesis

The Real Python Podcast

Jan 15, 2021 1h 2m

Nir Aides
Nir Aides

What is it like to design a Python library for three different audiences? This week on the show, we have Nir Aides, creator of Jupylet. His new library is designed for deep reinforcement learning researchers, musicians interested in live music coding, and kids interested in learning to program. Everything is designed to run inside of a Jupyter notebook.

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Nir’s initial goal was to create a framework to study deep reinforcement learning, and this led to building a framework for 2D and 3D games and graphics. As he continued the development, he realized that this interactive environment could be a useful tool for learning Python.

We also talk about how he got interested in live music coding and the advanced mathematics of sound synthesis. Nir also shares some resources for finding graphic assets and tools for creating 3D models.


  • 00:00:00 – Introduction
  • 00:02:25 – When did you start the project?
  • 00:02:50 – What is deep reinforcement learning?
  • 00:06:11 – How is deep reinforcement learning implemented in Jupylet?
  • 00:06:56 – What graphic libraries are being used?
  • 00:09:56 – What are the audiences for Jupylet?
  • 00:14:15 – Why create features for musicians?
  • 00:15:52 – Interactive code
  • 00:19:13 – Were you using Jupyter Notebooks previously?
  • 00:24:01 – Sponsor Digital Ocean
  • 00:24:40 – Scaling features and making it kid friendly
  • 00:28:59 – Outside help and learning about audio synthesis
  • 00:33:31 – Using NumPy for synthesis, effects, and algorithmic reverb
  • 00:39:08 – Video Course Spotlight
  • 00:40:13 – Relying on other packages for your own package
  • 00:42:26 – Assets for game design and working with 3D
  • 00:47:51 – What has feedback been like?
  • 00:48:31 – Looking for contributors
  • 00:49:45 – More on live music looping
  • 00:53:24 – What are you excited about in the world of Python?
  • 00:55:41 – What do you want to learn next?
  • 01:01:13 – Thanks and goodbye

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