|
I am a third-year undergraduate studying EECS at UC Berkeley. I work on reinforcement learning, decision making, and artificial intelligence as part of the Robot Learning Lab where I am fortunate to work with Igor Mordatch, Aditya Grover, and Pieter Abbeel. I am the head TA of EECS 126, Berkeley's class on probability and random processes. Email / Resume / Google Scholar / Github |
|
|
I am broadly interested in artificial intelligence. I am particularly interested in making learning more autonomous, such as enabling agents to learn in reset-free, nonstationary, or unsupervised settings; or by incorporating more sources of supervision, such as offline or multimodal data. |
|
Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch arXiv preprint, 2021 arXiv / blog / code unofficial: press / video (by Yannic Kilcher) We show that a transformer pretrained on natural language can, without finetuning of the self-attention and feedforward layers, match the performance of a transformed fully trained on a downstream non-language modality. |
 
|
Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch International Conference on Learning Representations, 2021 NeurIPS Deep RL Workshop, 2020 (Contributed Talk) arXiv / website / oral / poster / code We show planning over a space of skills is a key component of successful reset-free lifelong learning, avoiding sink states, improving stability, and increasing learning signal. |
 
|
Ruihan Zhao, Kevin Lu, Pieter Abbeel, Stas Tiomkin International Conference on Learning Representations, 2021 paper We design a new unbiased empowerment estimator and show it represents empowerment more faithfully than traditional variational mutual information algorithms. |
 
|
Kevin Lu, Igor Mordatch, Pieter Abbeel NeurIPS Deep RL Workshop, 2019 (Contributed Talk) arXiv / website / oral / poster / code Tackling reset-free learning in dynamically changing worlds by combining model-based planning with model-free learning, utilizing an expensive planner only when necessary. |
|
|
|
EECS 126: Probability and Random Processes
CS 70: Discrete Math and Probability |
|
|