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20182022
most citedInsights From the NeurIPS 2021 NetHack Challenge

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG20221 cited

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…

cs.AI2021

Deep Learning for General Game Playing with Ludii and Polygames

Dennis J. N. J. Soemers, Vegard Mella, Cameron Browne +1

Combinations of Monte-Carlo tree search and Deep Neural Networks, trained through self-play, have produced state-of-the-art results for automated game-playing in many board games.…

cs.LG2020

Polygames: Improved Zero Learning

Tristan Cazenave, Yen-Chi Chen, Guan-Wei Chen +21

Since DeepMind's AlphaZero, Zero learning quickly became the state-of-the-art method for many board games. It can be improved using a fully convolutional structure (no fully connec…

cs.LG2018

Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger

Gabriel Synnaeve, Zeming Lin, Jonas Gehring +5

We formulate the problem of defogging as state estimation and future state prediction from previous, partial observations in the context of real-time strategy games. We propose to…

cs.LG2018

High-Level Strategy Selection under Partial Observability in StarCraft: Brood War

Jonas Gehring, Da Ju, Vegard Mella +3

We consider the problem of high-level strategy selection in the adversarial setting of real-time strategy games from a reinforcement learning perspective, where taking an action co…