794 citations · 2.7k across the 93 of their papers we have counts for
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Exploration by Random Network Distillation
Yuri Burda, Harrison Edwards, Amos Storkey +1
We introduce an exploration bonus for deep reinforcement learning methods that is easy to implement and adds minimal overhead to the computation performed. The bonus is the error o…
How to train your MAML
Antreas Antoniou, Harrison Edwards, Amos Storkey
The field of few-shot learning has recently seen substantial advancements. Most of these advancements came from casting few-shot learning as a meta-learning problem. Model Agnostic…
Large-Scale Study of Curiosity-Driven Learning
Yuri Burda, Harri Edwards, Deepak Pathak +3
Reinforcement learning algorithms rely on carefully engineering environment rewards that are extrinsic to the agent. However, annotating each environment with hand-designed, dense…