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11/01/2022
Dungeons and Data: A Large-Scale NetHack Dataset
Recent breakthroughs in the development of agents to solve challenging s...
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07/11/2022
Grounding Aleatoric Uncertainty in Unsupervised Environment Design
Adaptive curricula in reinforcement learning (RL) have proven effective ...
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03/22/2022
Insights From the NeurIPS 2021 NetHack Challenge
In this report, we summarize the takeaways from the first NeurIPS 2021 N...
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09/27/2021
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research
The progress in deep reinforcement learning (RL) is heavily driven by th...
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02/13/2021
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them
Open-domain Question Answering models which directly leverage question-a...
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01/01/2021
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
We review the EfficientQA competition from NeurIPS 2020. The competition...
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06/24/2020
The NetHack Learning Environment
Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand wit...
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06/22/2020
Learning with AMIGo: Adversarially Motivated Intrinsic Goals
A key challenge for reinforcement learning (RL) consists of learning in ...
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05/22/2020
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Large pre-trained language models have been shown to store factual knowl...
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10/09/2019
MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions
Effective network congestion control strategies are key to keeping the I...
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10/08/2019
TorchBeast: A PyTorch Platform for Distributed RL
TorchBeast is a platform for reinforcement learning (RL) research in PyT...
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03/10/2018
Kickstarting Deep Reinforcement Learning
We present a method for using previously-trained 'teacher' agents to kic...
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08/16/2017
StarCraft II: A New Challenge for Reinforcement Learning
This paper introduces SC2LE (StarCraft II Learning Environment), a reinf...
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12/12/2016
