177 citations · 387 across the 12 of their papers we have counts for
12 papers
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors
William H. Guss, Mario Ynocente Castro, Sam Devlin +12
Although deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples, affording only a shrink…
Near-Optimal Online Egalitarian learning in General Sum Repeated Matrix Games
Aristide Tossou, Christos Dimitrakakis, Jaroslaw Rzepecki +1
We study two-player general sum repeated finite games where the rewards of each player are generated from an unknown distribution. Our aim is to find the egalitarian bargaining sol…
Fast Context Adaptation via Meta-Learning
Luisa M Zintgraf, Kyriacos Shiarlis, Vitaly Kurin +2
We propose CAVIA for meta-learning, a simple extension to MAML that is less prone to meta-overfitting, easier to parallelise, and more interpretable. CAVIA partitions the model par…
Depth and nonlinearity induce implicit exploration for RL
Justas Dauparas, Ryota Tomioka, Katja Hofmann
The question of how to explore, i.e., take actions with uncertain outcomes to learn about possible future rewards, is a key question in reinforcement learning (RL). Here, we show a…
Variational Inference for Data-Efficient Model Learning in POMDPs
Sebastian Tschiatschek, Kai Arulkumaran, Jan Stühmer +1
Partially observable Markov decision processes (POMDPs) are a powerful abstraction for tasks that require decision making under uncertainty, and capture a wide range of real world…
Cross Domain Regularization for Neural Ranking Models Using Adversarial Learning
Daniel Cohen, Bhaskar Mitra, Katja Hofmann +1
Unlike traditional learning to rank models that depend on hand-crafted features, neural representation learning models learn higher level features for the ranking task by training…