Using Wearable Sensors to Measure Motor Abilities following Stroke. - Todd Hester; Richard Hughes; Delsey M. Sherrill; Bethany Knorr; Metin Akay; Joel Stein; Paolo Bonato (BSN, 2006)

Using Wearable Sensors to Analyze the Quality of Use of Mobility Assistive Devices. - Todd Hester; Delsey M. Sherrill; Paolo Bonato; Mathieu Hamel; Karine Perreault; Patrick Boissy (BSN, 2006)

Analysis of the Severity of Dyskinesia in Patients with Parkinson's Disease via Wearable Sensors. - Shyamal Patel; Delsey M. Sherrill; Richard Hughes; Todd Hester; Theresa Lie-Nemeth; Paolo Bonato; David G. Standaert; Nancy Huggins (BSN, 2006)

Identification of Tasks Performed by Stroke Patients Using a Mobility Assistive Device. - Todd Hester; Delsey M. Sherrill; Mathieu Hamel; Karine Perreault; Patrick Boissy; Paolo Bonato (EMBC, 2006)

The utility of temporal abstraction in reinforcement learning. - Nicholas K. Jong; Todd Hester; Peter Stone 001 (AAMAS (1), 2008)

Negative information and line observations for Monte Carlo localization. - Todd Hester; Peter Stone 001 (ICRA, 2008)

Processing Wearable Sensor Data to Optimize Deep-Brain Stimulation. - Shyamal Patel; Todd Hester; Richard Hughes; Nancy Huggins; Alice Flaherty; David G. Standaert; John Growdon; Paolo Bonato (IEEE Pervasive Comput., 2008)

Generalized model learning for reinforcement learning in factored domains. - Todd Hester; Peter Stone 001 (AAMAS (2), 2009)

Three Humanoid Soccer Platforms: Comparison and Synthesis. - Shivaram Kalyanakrishnan; Todd Hester; Michael J. Quinlan; Yinon Bentor; Peter Stone 001 (RoboCup, 2009)

Real time targeted exploration in large domains. - Todd Hester; Peter Stone 001 (ICDL, 2010)

Generalized model learning for Reinforcement Learning on a humanoid robot. - Todd Hester; Michael J. Quinlan; Peter Stone 001 (ICRA, 2010)

A Novel Approach to Monitor Rehabilitation Outcomes in Stroke Survivors Using Wearable Technology. - Shyamal Patel; Richard Hughes; Todd Hester; Joel Stein; Metin Akay; Jennifer G. Dy; Paolo Bonato (Proc. IEEE, 2010)

A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control - Todd Hester; Michael J. Quinlan; Peter Stone 001 (CoRR, 2011)

Learning and Using Models. - Todd Hester; Peter Stone 001 (Reinforcement Learning, 2012)

Intrinsically motivated model learning for a developing curious agent. - Todd Hester; Peter Stone 001 (ICDL-EPIROB, 2012)

RTMBA: A Real-Time Model-Based Reinforcement Learning Architecture for robot control. - Todd Hester; Michael J. Quinlan; Peter Stone 001 (ICRA, 2012)

UT Austin Villa 2012: Standard Platform League World Champions. - Samuel Barrett; Katie Long Genter; Yuchen He 003; Todd Hester; Piyush Khandelwal; Jacob Menashe; Peter Stone 001 (RoboCup, 2012)

Learning exploration strategies in model-based reinforcement learning. - Todd Hester; Manuel Lopes 001; Peter Stone 001 (AAMAS, 2013)

The 2012 UT Austin Villa Code Release. - Samuel Barrett; Katie Long Genter; Yuchen He 003; Todd Hester; Piyush Khandelwal; Jacob Menashe; Peter Stone 001 (RoboCup, 2013)

The Open-Source TEXPLORE Code Release for Reinforcement Learning on Robots. - Todd Hester; Peter Stone 001 (RoboCup, 2013)

TEXPLORE: real-time sample-efficient reinforcement learning for robots. - Todd Hester; Peter Stone 001 (Mach. Learn., 2013)

TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains. - Todd Hester (Springer, 2013)

Adaptive Lambda Least-Squares Temporal Difference Learning. - Timothy A. Mann; Hugo Penedones; Shie Mannor; Todd Hester (CoRR, 2016)

Intrinsically motivated model learning for developing curious robots. - Todd Hester; Peter Stone 001 (Artif. Intell., 2017)

Learning from Demonstrations for Real World Reinforcement Learning. - Todd Hester; Matej Vecerík; Olivier Pietquin; Marc Lanctot; Tom Schaul; Bilal Piot; Andrew Sendonaris; Gabriel Dulac-Arnold; Ian Osband; John P. Agapiou; Joel Z. Leibo; Audrunas Gruslys (CoRR, 2017)

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards. - Matej Vecerík; Todd Hester; Jonathan Scholz; Fumin Wang; Olivier Pietquin; Bilal Piot; Nicolas Heess; Thomas Rothörl; Thomas Lampe; Martin A. Riedmiller (CoRR, 2017)

Deep Q-learning From Demonstrations. - Todd Hester; Matej Vecerík; Olivier Pietquin; Marc Lanctot; Tom Schaul; Bilal Piot; Dan Horgan; John Quan; Andrew Sendonaris; Ian Osband; Gabriel Dulac-Arnold; John P. Agapiou; et al. (AAAI, 2018)

Safe Exploration in Continuous Action Spaces. - Gal Dalal; Krishnamurthy Dvijotham; Matej Vecerík; Todd Hester; Cosmin Paduraru; Yuval Tassa (CoRR, 2018)

Observe and Look Further: Achieving Consistent Performance on Atari. - Tobias Pohlen; Bilal Piot; Todd Hester; Mohammad Gheshlaghi Azar; Dan Horgan; David Budden; Gabriel Barth-Maron; Hado van Hasselt; John Quan; Mel Vecerík; Matteo Hessel; Rémi Munos; et al. (CoRR, 2018)

A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning. - Mel Vecerík; Oleg Sushkov; David Barker; Thomas Rothörl; Todd Hester; Jonathan Scholz (ICRA, 2019)

Challenges of Real-World Reinforcement Learning. - Gabriel Dulac-Arnold; Daniel J. Mankowitz; Todd Hester (CoRR, 2019)

Robust Reinforcement Learning for Continuous Control with Model Misspecification. - Daniel J. Mankowitz; Nir Levine; Rae Jeong; Abbas Abdolmaleki; Jost Tobias Springenberg; Timothy A. Mann; Todd Hester; Martin A. Riedmiller (CoRR, 2019)

Robust Reinforcement Learning for Continuous Control with Model Misspecification. - Daniel J. Mankowitz; Nir Levine; Rae Jeong; Abbas Abdolmaleki; Jost Tobias Springenberg; Yuanyuan Shi; Jackie Kay; Todd Hester; Timothy A. Mann; Martin A. Riedmiller (ICLR, 2020)

An empirical investigation of the challenges of real-world reinforcement learning. - Gabriel Dulac-Arnold; Nir Levine; Daniel J. Mankowitz; Jerry Li; Cosmin Paduraru; Sven Gowal; Todd Hester (CoRR, 2020)

ETA Prediction with Graph Neural Networks in Google Maps. - Austin Derrow-Pinion; Jennifer She; David Wong; Oliver Lange; Todd Hester; Luis Perez; Marc Nunkesser; Seongjae Lee; Xueying Guo; Brett Wiltshire; Peter W. Battaglia; Vishal Gupta; et al. (CIKM, 2021)

Challenges of real-world reinforcement learning: definitions, benchmarks and analysis. - Gabriel Dulac-Arnold; Nir Levine; Daniel J. Mankowitz; Jerry Li; Cosmin Paduraru; Sven Gowal; Todd Hester (Mach. Learn., 2021)