10.1k citations · 13.1k across the 22 of their papers we have counts for
7 papers · 1 filter
GraphCast: Learning skillful medium-range global weather forecasting
Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson +15
Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute reso…
MultiScale MeshGraphNets
Meire Fortunato, Tobias Pfaff, Peter Wirnsberger +2
In recent years, there has been a growing interest in using machine learning to overcome the high cost of numerical simulation, with some learned models achieving impressive speed-…
Never Give Up: Learning Directed Exploration Strategies
Adrià Puigdomènech Badia, Pablo Sprechmann, Alex Vitvitskyi +8
We propose a reinforcement learning agent to solve hard exploration games by learning a range of directed exploratory policies. We construct an episodic memory-based intrinsic rewa…
Meta-learning of Sequential Strategies
Pedro A. Ortega, Jane X. Wang, Mark Rowland +21
In this report we review memory-based meta-learning as a tool for building sample-efficient strategies that learn from past experience to adapt to any task within a target class. O…
Fast deep reinforcement learning using online adjustments from the past
Steven Hansen, Pablo Sprechmann, Alexander Pritzel +2
We propose Ephemeral Value Adjusments (EVA): a means of allowing deep reinforcement learning agents to rapidly adapt to experience in their replay buffer. EVA shifts the value pred…
Deep Exploration via Bootstrapped DQN
Ian Osband, Charles Blundell, Alexander Pritzel +1
Efficient exploration in complex environments remains a major challenge for reinforcement learning. We propose bootstrapped DQN, a simple algorithm that explores in a computational…