Computer Scientist with seven years of engineering experience based in Los Angeles and a USC Computer Science background (Class of 2022) who bridges research environments and production-quality tooling. Skilled in test automation and CI, they strengthened the testing infrastructure for the widely used rlworkgroup/garage reinforcement learning toolkit—adding doctests, hardening GitHub Actions workflows, and preserving important numerical logging across CPU/GPU runs. Comfortable working at the intersection of research labs (INK Lab, RESL) and engineering, they bring a researcher’s attention to reproducibility together with pragmatic QA practices that prevent regressions in complex ML codebases. Notably, they focus on reliability and maintainability in open-source ML projects, turning brittle experimental code into dependable components for the broader community.
A toolkit for reproducible reinforcement learning research.
Role in this project:
QA Engineer / Test Automation Engineer
Contributions:15 reviews, 88 commits, 43 PRs in 9 months
Contributions summary:ziyiwu9494 primarily contributed to improving the testing infrastructure and test coverage of the repository. They added doctests for Continuous Integration (CI), updated existing test suites, and corrected CI scripts. The user also worked on ensuring that test suites run correctly with GitHub Actions and made changes to address potential issues within the testing frameworks. They also preserved the log alpha for Sac implementation when moving between CPU and GPU.
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