Search Results for author: Heinrich Küttler

Found 14 papers, 13 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

Grounding Aleatoric Uncertainty for Unsupervised Environment Design

1 code implementation11 Jul 2022 Minqi Jiang, Michael Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel, Jakob Foerster

Problematically, in partially-observable or stochastic settings, optimal policies may depend on the ground-truth distribution over aleatoric parameters of the environment in the intended deployment setting, while curriculum learning necessarily shifts the training distribution.

Reinforcement Learning (RL)

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)

MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research

1 code implementation27 Sep 2021 Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel

By leveraging the full set of entities and environment dynamics from NetHack, one of the richest grid-based video games, MiniHack allows designing custom RL testbeds that are fast and convenient to use.

NetHack reinforcement-learning +2

The NetHack Learning Environment

3 code implementations NeurIPS 2020 Heinrich Küttler, Nantas Nardelli, Alexander H. Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, Tim Rocktäschel

Here, we present the NetHack Learning Environment (NLE), a scalable, procedurally generated, stochastic, rich, and challenging environment for RL research based on the popular single-player terminal-based roguelike game, NetHack.

NetHack Score Reinforcement Learning (RL) +1

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