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Jake Grigsby I am a third year CS PhD student at UT Austin, working with Prof. Yuke Zhu and the Robot Perception and Learning Lab . My research focuses on generalization and long-term memory in deep reinforcement learning. Before coming to Austin, I studied Math and CS at the University of Virginia, where my research was advised by Prof. Yanjun Qi .
Research VLM Q-Learning: Aligning Vision-Language Models for Interactive Decision-Making
Grigsby, Jake , Zhu, Yuke, Ryoo, Michael S, and Niebles, Juan Carlos
SSI-FM Workshop at ICLR 2025
AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers
Grigsby, Jake , Sasek, Justin, Parajuli, Samyak, Adebi, Daniel, Zhang, Amy and 1 more author
NeurIPS 2024
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agents
Grigsby, Jake , Fan, Jim, and Zhu, Yuke
ICLR (Spotlight) 2024
Cross-Episodic Curriculum for Transformer Agents
Shi, Lucy Xiaoyang, Jiang, Yunfan, Grigsby, Jake , Fan, Jim, and Zhu, Yuke
NeurIPS 2023
PGrad: Learning Principal Gradients For Domain Generalization
Wang, Zhe, Grigsby, Jake , and Qi, Yanjun
ICLR 2022
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning
Wang, Zhe, Grigsby, Jake , Sekhon, Arshdeep, and Qi, Yanjun
Conference on Uncertainty in Artificial Intelligence 2022
RARE: Renewable Energy Aware Resource Management in Datacenters
Venkataswamy, Vanamala, Grigsby, Jake , Grimshaw, Andrew, and Qi, Yanjun
Workshop on Job Scheduling for Parallel Processing 2022
Towards Automatic Actor-Critic Solutions to Continuous Control
Grigsby, Jake , Yoo, Jin Yong, and Qi, Yanjun
NeurIPS Workshop on Deep Reinforcement Learning 2021
A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise Datasets
Grigsby, Jake , and Qi, Yanjun
UVA Distinguished Major Thesis 2021
Deep learning analysis of deeply virtual exclusive photoproduction
Grigsby, Jake , Kriesten, Brandon, Hoskins, Joshua, Liuti, Simonetta, Alonzi, Peter and 1 more author
Phys. Rev. D 2021
Measuring Visual Generalization in Continuous Control From Pixels
Grigsby, Jake , and Qi, Yanjun
NeurIPS Workshop on Deep Reinforcement Learning 2020
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-LearningConference on Uncertainty in Artificial Intelligence 2022
RARE: Renewable Energy Aware Resource Management in DatacentersWorkshop on Job Scheduling for Parallel Processing 2022
A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise DatasetsUVA Distinguished Major Thesis 2021
