Search Results for author: Vegard Mella

Found 8 papers, 5 papers with code

Dungeons and Data: A Large-Scale NetHack Dataset

1 code implementation1 Nov 2022 Eric Hambro, Roberta Raileanu, Danielle Rothermel, Vegard Mella, Tim Rocktäschel, Heinrich Küttler, Naila Murray

Recent breakthroughs in the development of agents to solve challenging sequential decision making problems such as Go, StarCraft, or DOTA, have relied on both simulated environments and large-scale datasets.

Decision Making NetHack +2

moolib: A Platform for Distributed RL

1 code implementation26 Jan 2022 Vegard Mella, Eric Hambro, Danielle Rothermel, Heinrich Küttler

Together with the moolib library, we present example user code which shows how moolib’s components can be used to implement common reinforcement learning agents as a simple but scalable distributed network of homogeneous peers.

reinforcement-learning Reinforcement Learning (RL)

Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants

no code implementations24 Feb 2021 Dennis J. N. J. Soemers, Vegard Mella, Eric Piette, Matthew Stephenson, Cameron Browne, Olivier Teytaud

In this paper, we use fully convolutional architectures in AlphaZero-like self-play training setups to facilitate transfer between variants of board games as well as distinct games.

Board Games Transfer Learning

Deep Learning for General Game Playing with Ludii and Polygames

1 code implementation23 Jan 2021 Dennis J. N. J. Soemers, Vegard Mella, Cameron Browne, Olivier Teytaud

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.

Board Games

Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger

1 code implementation ICLR 2018 Gabriel Synnaeve, Zeming Lin, Jonas Gehring, Dan Gant, Vegard Mella, Vasil Khalidov, Nicolas Carion, Nicolas Usunier

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.


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

no code implementations21 Nov 2018 Jonas Gehring, Da Ju, Vegard Mella, Daniel Gant, Nicolas Usunier, Gabriel Synnaeve

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 corresponds to switching to the respective strategy.

reinforcement-learning Reinforcement Learning (RL) +2

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