86 citations · 91 across the 4 of their papers we have counts for
6 papers
Probing Transfer in Deep Reinforcement Learning without Task Engineering
Andrei A. Rusu, Sebastian Flennerhag, Dushyant Rao +2
We evaluate the use of original game curricula supported by the Atari 2600 console as a heterogeneous transfer benchmark for deep reinforcement learning agents. Game designers crea…
MO2: Model-Based Offline Options
Sasha Salter, Markus Wulfmeier, Dhruva Tirumala +4
The ability to discover useful behaviours from past experience and transfer them to new tasks is considered a core component of natural embodied intelligence. Inspired by neuroscie…
Task-agnostic Continual Learning with Hybrid Probabilistic Models
Polina Kirichenko, Mehrdad Farajtabar, Dushyant Rao +6
Learning new tasks continuously without forgetting on a constantly changing data distribution is essential for real-world problems but extremely challenging for modern deep learnin…
Attention-Privileged Reinforcement Learning
Sasha Salter, Dushyant Rao, Markus Wulfmeier +2
Image-based Reinforcement Learning is known to suffer from poor sample efficiency and generalisation to unseen visuals such as distractors (task-independent aspects of the observat…
Continual Unsupervised Representation Learning
Dushyant Rao, Francesco Visin, Andrei A. Rusu +3
Continual learning aims to improve the ability of modern learning systems to deal with non-stationary distributions, typically by attempting to learn a series of tasks sequentially…
Meta-Learning with Latent Embedding Optimization
Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski +4
Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have practica…