activity
20182021
most citedSafe Exploration in Continuous Action Spaces

275 citations · 738 across the 3 of their papers we have counts for

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5 papers · 1 filter

cs.LG2021209 cited

ETA Prediction with Graph Neural Networks in Google Maps

Austin Derrow-Pinion, Jennifer She, David Wong +14

Travel-time prediction constitutes a task of high importance in transportation networks, with web mapping services like Google Maps regularly serving vast quantities of travel time…

cs.LG2020

An empirical investigation of the challenges of real-world reinforcement learning

Gabriel Dulac-Arnold, Nir Levine, Daniel J. Mankowitz +4

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is beginning to show some successes in real-world scenarios. However, much of the research a…

cs.LG2019

Robust Reinforcement Learning for Continuous Control with Model Misspecification

Daniel J. Mankowitz, Nir Levine, Rae Jeong +7

We provide a framework for incorporating robustness -- to perturbations in the transition dynamics which we refer to as model misspecification -- into continuous control Reinforcem…

cs.LG2019254 cited

Challenges of Real-World Reinforcement Learning

Gabriel Dulac-Arnold, Daniel Mankowitz, Todd Hester

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is beginning to show some successes in real-world scenarios. However, much of the research a…

cs.LG2018

Observe and Look Further: Achieving Consistent Performance on Atari

Tobias Pohlen, Bilal Piot, Todd Hester +10

Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks…