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01/18/2023
Human-Timescale Adaptation in an Open-Ended Task Space
Foundation models have shown impressive adaptation and scalability in su...
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07/05/2022
An Empirical Study of Implicit Regularization in Deep Offline RL
Deep neural networks are the most commonly used function approximators i...
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07/27/2021
Open-Ended Learning Leads to Generally Capable Agents
In this work we create agents that can perform well beyond a single, ind...
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03/17/2021
Regularized Behavior Value Estimation
Offline reinforcement learning restricts the learning process to rely on...
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10/06/2020
Sequential Changepoint Detection in Neural Networks with Checkpoints
We introduce a framework for online changepoint detection and simultaneo...
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09/10/2020
Importance Weighted Policy Learning and Adaption
The ability to exploit prior experience to solve novel problems rapidly ...
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06/12/2019
Task Agnostic Continual Learning via Meta Learning
While neural networks are powerful function approximators, they suffer f...
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07/16/2018
Meta-Learning with Latent Embedding Optimization
Gradient-based meta-learning techniques are both widely applicable and p...
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06/06/2018
Meta Learning by the Baldwin Effect
The scope of the Baldwin effect was recently called into question by two...
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05/09/2016