Kevin Lu

I am an AI resident at Facebook AI Research, advised by Amy Zhang and Yuandong Tian. My research interests are in sequential decision making, as well as universality in deep learning and AI. I previously graduated with my B.S. in Electrical Engineering and Computer Science from UC Berkeley in 2021, where I did undergraduate research as part of the Robot Learning Lab advised by Igor Mordatch and Pieter Abbeel.

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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.

Decision Transformer: Reinforcement Learning via Sequence Modeling
Lili Chen*, Kevin Lu*, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas*, Igor Mordatch*
arXiv preprint, 2021
arXiv / website / code
press (SyncedReview) / press (The Gradient) / video (Yannic Kilcher) / seminar (Eindhoven RL)

We show that a simple transformer trained with a sequence modeling objective can perform competitively with strong specialized offline RL methods.

Pretrained Transformers as Universal Computation Engines
Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch
arXiv preprint, 2021
arXiv / blog / code
press (The Batch) / press (VentureBeat) / podcast (TWIML) / video (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.

Reset-Free Lifelong Learning with Skill-Space Planning
Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch
International Conference on Learning Representations, 2021
NeurIPS Deep RL Workshop, 2020   (Contributed Talk)
ICLR Never-Ending Reinforcement Learning Workshop, 2021   (Invited Paper)
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.

Efficient Empowerment Estimation for Unsupervised Stabilization
Ruihan Zhao, Kevin Lu, Pieter Abbeel, Stas Tiomkin
International Conference on Learning Representations, 2021
arXiv / website / code

We design a new unbiased empowerment estimator and show it represents empowerment more faithfully than traditional variational mutual information algorithms.

Adaptive Online Planning for Continual Lifelong Learning
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
Head Teaching Assistant: Spring 2021, Fall 2020
Teaching Assistant: Spring 2020, Fall 2019

CS 70: Discrete Math and Probability
Reader: Spring 2019

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