Max Sobol Mark

I am a Master’s student in Computer Science at Stanford University and a Research Assistant at IRIS Lab. The goal of my research is to develop generally capable robots that can use many skills in any new scene. I am excited to develop Reinforcement Learning methods that can take advantage of large, broad datasets that include suboptimal data.

Email | Resume | Scholar | Github

Research

	Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning Jingyun Yang, Max Sobol Mark, Brandon Vu, Archit Sharma, Jeannette Bohg, Chelsea Finn Preprint, under review, 2023 project page / video / arXiv
	Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration Bias Max Sobol Mark, Archit Sharma, Fahim Tajwar, Rafael Rafailov, Sergey Levine, Chelsea Finn Preprint, under review, 2023 arXiv / code
	Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning Mitsuhiko Nakamoto, Yuexiang Zhai, Anikait Singh, Max Sobol Mark, Yi Ma, Chelsea Finn, Aviral Kumar, Sergey Levine NeurIPS, 2023 project page / video / arXiv / code
	Fine-tuning Offline Policies with Optimistic Action Selection Max Sobol Mark, Ali Ghadirzadeh, Xi Chen, Chelsea Finn NeurIPS DeepRL Workshop, 2022 Paper
	Unsupervised Learning from Video with Deep Neural Embeddings Chengxu Zhuang, Tianwei She, Alex Andonian, Max Sobol Mark, Daniel Yamins CVPR, 2020 Paper / Code

Teaching

CS330: Deep Multi-Task and Meta Learning
Course Assistant, Stanford University, Fall 2022 and 2023

CS234: Reinforcement Learning
Course Assistant, Stanford University, Winter 2023

CS224R: Deep Reinforcement Learning
Course Assistant, Stanford University, Spring 2023

Last updated: 2024-01-07. Website template .