R Zentner
Senior Research Scientist at Nvidia Corporation(US)
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolR Zentner is a Senior Research Scientist based in Mountain View with 7 years of experience at the intersection of reinforcement learning, robotics, and scalable software engineering. He holds a PhD in Computer Science from USC and a BS in EECS from UC Berkeley, and has transitioned from research roles (USC, Google Brain) into industry R&D at Nvidia after shaping robotic learning at 1X. His open-source contributions include refactoring core RL tooling in the widely-used garage repository and improving Meta-World robotics environments, demonstrating a focus on reproducibility, environment robustness, and API design. Colleagues describe him through strengths like Maximizer, Learner, and Individualization—traits reflected in his attention to consistency, seeding controls, and cleaner integration with libraries like akro. Comfortable across backend, ML engineering, and research, he blends rigorous academic thinking with pragmatic code-level improvements that make RL benchmarks and robotics experiments more reliable.
7 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of Southern CaliforniaGrossmont CollegeBS, Electrical Engineering and Computer Science, BS, Electrical Engineering and Computer Science at UC Berkeley
French, Russian
Github Skills (19)
environmental10
dev-environment10
python10
gymnasium10
openai-gym10
machine-learning10
reinforcement-learning10
enviroment10
mujoco10
environ10
algorithm9
pytorch9
algorithms9
tensorflow9
repr8
Programming languages (6)
Github contributions (5)
rlworkgroup/garage
Feb 2019 - Jun 2022
A toolkit for reproducible reinforcement learning research.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:126 reviews, 185 commits, 188 PRs in 3 years 3 months
Contributions summary:R primarily refactored the codebase to utilize the `akro` library and updated imports to align with the library's guidelines. These changes focused on refactoring the environment and policy modules within the garage repository to incorporate functionalities from the `akro` library, specifically related to the integration of gym environments and the handling of spaces. The user also made modifications to examples to adapt to the new `akro` integration, ensuring the codebase's compatibility with the core libraries and tools used for reinforcement learning research.
pytorchreinforcement-learningreproduciblereproducibilityrl-algorithms
Farama-Foundation/Metaworld
Mar 2020 - Jan 2023
Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning
Role in this project:ML Engineer
ML EngineerContributions:7 reviews, 53 commits, 23 PRs in 2 years 9 months
Contributions summary:R primarily focused on modifying and implementing features within the metaworld environment. They disabled a "done" signal and fixed potential errors related to it, improving the environment's consistency. The user also addressed issues related to setting random initialization flags and implemented a new API, simplifying usage and fixing bugs. Furthermore, the user added a "seeded_rand_vec" flag for enhanced control.
manipulationmulti-taskroboticsbenchmarkingmultiagent-reinforcement-learning
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.