Anderson's orthogonality catastrophe. - Heinrich Küttler (2014)

DeepMind Lab. - Charles Beattie; Joel Z. Leibo; Denis Teplyashin; Tom Ward; Marcus Wainwright; Heinrich Küttler; Andrew Lefrancq; Simon Green; Víctor Valdés; Amir Sadik; Julian Schrittwieser; Keith Anderson; et al. (CoRR, 2016)

StarCraft II: A New Challenge for Reinforcement Learning. - Oriol Vinyals; Timo Ewalds; Sergey Bartunov; Petko Georgiev; Alexander Sasha Vezhnevets; Michelle Yeo; Alireza Makhzani; Heinrich Küttler; John P. Agapiou; Julian Schrittwieser; John Quan; Stephen Gaffney; et al. (CoRR, 2017)

Kickstarting Deep Reinforcement Learning. - Simon Schmitt; Jonathan J. Hudson; Augustin Zídek; Simon Osindero; Carl Doersch; Wojciech M. Czarnecki; Joel Z. Leibo; Heinrich Küttler; Andrew Zisserman; Karen Simonyan; S. M. Ali Eslami (CoRR, 2018)

TorchBeast: A PyTorch Platform for Distributed RL. - Heinrich Küttler; Nantas Nardelli; Thibaut Lavril; Marco Selvatici; Viswanath Sivakumar; Tim Rocktäschel; Edward Grefenstette (CoRR, 2019)

MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions. - Viswanath Sivakumar; Tim Rocktäschel; Alexander H. Miller; Heinrich Küttler; Nantas Nardelli; Mike Rabbat; Joelle Pineau; Sebastian Riedel 001 (CoRR, 2019)

The NetHack Learning Environment. - Heinrich Küttler; Nantas Nardelli; Alexander H. Miller; Roberta Raileanu; Marco Selvatici; Edward Grefenstette; Tim Rocktäschel (NeurIPS, 2020)

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. - Patrick Lewis 001; Ethan Perez; Aleksandra Piktus; Fabio Petroni; Vladimir Karpukhin; Naman Goyal 001; Heinrich Küttler; Mike Lewis; Wen-tau Yih; Tim Rocktäschel; Sebastian Riedel 001; Douwe Kiela (NeurIPS, 2020)

NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned. - Sewon Min; Jordan L. Boyd-Graber; Chris Alberti; Danqi Chen 001; Eunsol Choi; Michael Collins 001; Kelvin Guu; Hannaneh Hajishirzi; Kenton Lee; Jennimaria Palomaki; Colin Raffel; Adam Roberts; et al. (NeurIPS (Competition and Demos), 2020)

Learning with AMIGo: Adversarially Motivated Intrinsic Goals. - Andres Campero; Roberta Raileanu; Heinrich Küttler; Joshua B. Tenenbaum; Tim Rocktäschel; Edward Grefenstette (ICLR, 2021)

Insights From the NeurIPS 2021 NetHack Challenge. - Eric Hambro; Sharada P. Mohanty; Dmitrii Babaev; Minwoo Byeon; Dipam Chakraborty; Edward Grefenstette; Minqi Jiang; DaeJin Jo; Anssi Kanervisto; Jongmin Kim 006; Sungwoong Kim; Robert Kirk; et al. (NeurIPS (Competition and Demos), 2021)

MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research. - Mikayel Samvelyan; Robert Kirk; Vitaly Kurin; Jack Parker-Holder; Minqi Jiang; Eric Hambro; Fabio Petroni; Heinrich Küttler; Edward Grefenstette; Tim Rocktäschel (NeurIPS Datasets and Benchmarks, 2021)

PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them. - Patrick Lewis 001; Yuxiang Wu; Linqing Liu; Pasquale Minervini; Heinrich Küttler; Aleksandra Piktus; Pontus Stenetorp; Sebastian Riedel 001 (Trans. Assoc. Comput. Linguistics, 2021)

Dungeons and Data: A Large-Scale NetHack Dataset. - Eric Hambro; Roberta Raileanu; Danielle Rothermel; Vegard Mella; Tim Rocktäschel; Heinrich Küttler; Naila Murray (NeurIPS, 2022)

Grounding Aleatoric Uncertainty for Unsupervised Environment Design. - Minqi Jiang; Michael Dennis 001; Jack Parker-Holder; Andrei Lupu; Heinrich Küttler; Edward Grefenstette; Tim Rocktäschel; Jakob N. Foerster (NeurIPS, 2022)