Using Unity to Help Solve Reinforcement Learning
Connor Brennan, Andrew Williams, Omar G. Younis, Vedant Vyas, Daria Yasafova +1 more
2/3/2026
No confident code implementation found for this paper.
No confident code implementation found for this paper.
No confident code match yet
We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.
No code implementations found yet.
Know of an implementation? Let us know in the comments below!
A Direct Approach to Viewing Graph Solvability
Streamlining Redundant Layers to Compress Large Language Models
Adversarial Text to Continuous Image Generation
Variational Counterfactual Intervention Planning to Achieve Target Outcomes
Teaching Arithmetic to Small Transformers
MAP IT to Visualize Representations
@article{brennan2026using,
title = {Using Unity to Help Solve Reinforcement Learning},
author = {Connor Brennan and Andrew Williams and Omar G. Younis and Vedant Vyas and Daria Yasafova and Irina Rish},
year = {2026},
url = {https://www.semanticscholar.org/paper/0572105a75b06ab504ee8f0e711e9d0ca0adc522},
journal = {NEURIPS 2024 2024}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/0572105a75b06ab504ee8f0e711e9d0ca0adc522)