John Balis
Platform Engineer
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJohn Balis is a software engineer with nine years of experience and a generalist programmer passionate about free and open-source artificial intelligence. He contributes to high-profile ML tooling such as OpenAI Gym, where he implemented and tested enhancements to the reset API (return_info), added an auto-reset wrapper, and updated async/sync vector environments. His contributions emphasize reinforcement-learning developer ergonomics, reproducibility around environment seeding, and careful deprecation handling. Practical and detail-oriented, he blends hands-on engineering with community-minded open-source collaboration.
9 years of coding experience
2 years of employment as a software developer
University of Wisconsin-Madison
English, Japanese
Github Skills (9)
gymnasium10
open-ai-gym10
environmental10
dev-environment10
environ10
python10
reinforcement-learning10
enviroment10
unit-testing9
Programming languages (12)
Github contributions (5)
openai/gym
Feb 2022 - Aug 2022
A toolkit for developing and comparing reinforcement learning algorithms.
Role in this project:
ML Engineer
ML EngineerContributions:47 reviews, 6 commits, 8 PRs in 6 months
Contributions summary:John primarily contributed to the Gym library by implementing and testing features related to the `reset` function, adding the ability to return an info dictionary. They modified the `async_vector_env.py`, `sync_vector_env.py`, and wrapper files to integrate the new `return_info` functionality. Additionally, the user implemented an auto-reset wrapper and made changes related to the handling of environment seeding, as well as removing the deprecated `seed` function from the vector environments.
comparingreinforcement-learning-algorithmsdevelopingdeep-learningreinforcement-learning
balisujohn/mltests
Sep 2018 - Apr 2020
This is a project with the loose goal of finding interesting ways to generate intelligent agents through trial and error.
Contributions:131 commits, 10 PRs, 113 pushes in 1 year 7 months
intelligent-agentsgoalinterestingfindingtrial-and-error
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