Reinforcement Learning and Flow Matching for End-to-End Driving
2026Driving policies trained end to end, combining reinforcement learning with diffusion and flow-matching policy classes.
Brayden Zhang
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I study Engineering Science at the University of Toronto, and work as a software engineer at Tenstorrent on distributed ML and low-latency inference.
I am a student researcher with the University of Toronto and NVIDIA Research, working on end-to-end driving, reinforcement learning, and diffusion and flow-matching models. Previously I was a machine learning intern at Ode, with Clay and LGND AI, finetuning geospatial foundation models.
My interests are in applications of AI to the physical world — geospatial machine learning and autonomous vehicles in particular.
Posts 3
Courses I've Taken in University · A Collection of Cool Companies (to me) · International Geography Olympiad 2023
A Collection of Cool Companies (to me)
July 2025International Geography Olympiad 2023
August 2023Notes
Machine Learning · Reinforcement Learning · ML Systems · Robotics · Math · Algorithms
A linked, searchable notebook — browse all of it
Machine Learning
Reinforcement Learning
ML Systems
Robotics
Geospatial
Mathematics
Algorithms
Systems Software
Research 3
End-to-end driving with RL and flow matching · RealADSim challenge, 1st place · Geospatial foundation models
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Closed-Loop End-to-End Driving in RealADSim
2025Our winning entry to the closed-loop driving challenge, evaluated in a reconstructed-real-world simulator rather than on logged trajectories.
Finetuning Geospatial Foundation Models
2025Adapted open geospatial foundation models to downstream Earth-observation tasks, and built the evaluation around them.
Projects 4
AlphaEarth Foundations · Predicting food deserts · Solar PV forecasting · IGeo
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AlphaEarth Foundations
2025A from-scratch implementation of DeepMind's embedding model for Earth observation.
International Geography Olympiad
2023Represented Canada at the International Geography Olympiad.