Since reinforcement learning requires hefty compute resources, it can be tough to keep up without a serious budget of your own. Find out how the team at Facebook AI Research (FAIR) is looking to increase access and level the playing field with the help of NetHack, an archaic rogue-like video game from the late 80s.

Links discussed:

The NetHack Learning Environment:

https://ai.facebook.com/blog/nethack-learning-environment-to-advance-deep-reinforcement-learning/

Reinforcement learning, intrinsic motivation:

https://arxiv.org/abs/2002.12292

Knowledge transfer:

https://arxiv.org/abs/1910.08210

Tim Rocktäschel is a Research Scientist at Facebook AI Research (FAIR) London and a Lecturer in the Department of Computer Science at University College London (UCL). At UCL, he is a member of the UCL Centre for Artificial Intelligence and the UCL Natural Language Processing group. Prior to that, he was a Postdoctoral Researcher in the Whiteson Research Lab, a Stipendiary Lecturer in Computer Science at Hertford College, and a Junior Research Fellow in Computer Science at Jesus College, at the University of Oxford.

https://twitter.com/_rockt

Heinrich Kuttler is an AI and machine learning researcher at Facebook AI Research (FAIR) and before that was a research engineer and team lead at DeepMind.

https://twitter.com/HeinrichKuttler

https://www.linkedin.com/in/heinrich-kuttler/

Topics covered:

0:00 a lack of reproducibility in RL

1:05 What is NetHack and how did the idea come to be?

5:46 RL in Go vs NetHack

11:04 performance of vanilla agents, what do you optimize for

18:36 transferring domain knowledge, source diving

22:27 human vs machines intrinsic learning

28:19 ICLR paper - exploration and RL strategies

35:48 the future of reinforcement learning

43:18 going from supervised to reinforcement learning

45:07 reproducibility in RL

50:05 most underrated aspect of ML, biggest challenges?

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