90 citations · 189 across the 7 of their papers we have counts for
5 papers
Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning
Michael Matthews, Mikayel Samvelyan, Jack Parker-Holder +2
Practising and honing skills forms a fundamental component of how humans learn, yet artificial agents are rarely specifically trained to perform them. Instead, they are usually tra…
GriddlyJS: A Web IDE for Reinforcement Learning
Christopher Bamford, Minqi Jiang, Mikayel Samvelyan +1
Progress in reinforcement learning (RL) research is often driven by the design of new, challenging environments -- a costly undertaking requiring skills orthogonal to that of a typ…
Grounding Aleatoric Uncertainty for Unsupervised Environment Design
Minqi Jiang, Michael Dennis, Jack Parker-Holder +5
Adaptive curricula in reinforcement learning (RL) have proven effective for producing policies robust to discrepancies between the train and test environment. Recently, the Unsuper…
Learning Python Code Suggestion with a Sparse Pointer Network
Avishkar Bhoopchand, Tim Rocktäschel, Earl Barr +1
To enhance developer productivity, all modern integrated development environments (IDEs) include code suggestion functionality that proposes likely next tokens at the cursor. While…
emoji2vec: Learning Emoji Representations from their Description
Ben Eisner, Tim Rocktäschel, Isabelle Augenstein +2
Many current natural language processing applications for social media rely on representation learning and utilize pre-trained word embeddings. There currently exist several public…