445 citations · 1.2k across the 18 of their papers we have counts for
9 papers · 1 filter
Insights From the NeurIPS 2021 NetHack Challenge
Eric Hambro, Sharada Mohanty, Dmitrii Babaev +26
In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge. Participants were tasked with developing a program or agent that can win (i.e., 'ascend' i…
The NetHack Learning Environment
Heinrich Küttler, Nantas Nardelli, Alexander H. Miller +4
Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand with the development of challenging environments that test the limits of current methods. While existing RL env…
Simulation-Based Inference for Global Health Decisions
Christian Schroeder de Witt, Bradley Gram-Hansen, Nantas Nardelli +8
The COVID-19 pandemic has highlighted the importance of in-silico epidemiological modelling in predicting the dynamics of infectious diseases to inform health policy and decision m…
TorchBeast: A PyTorch Platform for Distributed RL
Heinrich Küttler, Nantas Nardelli, Thibaut Lavril +4
TorchBeast is a platform for reinforcement learning (RL) research in PyTorch. It implements a version of the popular IMPALA algorithm for fast, asynchronous, parallel training of R…
MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions
Viswanath Sivakumar, Olivier Delalleau, Tim Rocktäschel +6
Effective network congestion control strategies are key to keeping the Internet (or any large computer network) operational. Network congestion control has been dominated by hand-c…
A Survey of Reinforcement Learning Informed by Natural Language
Jelena Luketina, Nantas Nardelli, Gregory Farquhar +5
To be successful in real-world tasks, Reinforcement Learning (RL) needs to exploit the compositional, relational, and hierarchical structure of the world, and learn to transfer it…
Multitask Soft Option Learning
Maximilian Igl, Andrew Gambardella, Jinke He +4
We present Multitask Soft Option Learning(MSOL), a hierarchical multitask framework based on Planning as Inference. MSOL extends the concept of options, using separate variational…
The StarCraft Multi-Agent Challenge
Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt +7
In the last few years, deep multi-agent reinforcement learning (RL) has become a highly active area of research. A particularly challenging class of problems in this area is partia…
TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games
Gabriel Synnaeve, Nantas Nardelli, Alex Auvolat +5
We present TorchCraft, a library that enables deep learning research on Real-Time Strategy (RTS) games such as StarCraft: Brood War, by making it easier to control these games from…