Search Results - "Hester, Todd"

Intrinsically motivated model learning for developing curious robots by Hester, Todd, Stone, Peter

“…Reinforcement Learning (RL) agents are typically deployed to learn a specific, concrete task based on a pre-defined reward function. However, in some cases an…”
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TEXPLORE: real-time sample-efficient reinforcement learning for robots by Hester, Todd, Stone, Peter

“…The use of robots in society could be expanded by using reinforcement learning (RL) to allow robots to learn and adapt to new situations online. RL is a…”
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TEXPLORE: real-time sample-efficient reinforcement learning for robots by HESTER, Todd, STONE, Peter

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

“…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,…”
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A Novel Approach to Monitor Rehabilitation Outcomes in Stroke Survivors Using Wearable Technology by Patel, Shyamal, Hughes, Richard, Hester, Todd, Stein, Joel, Akay, Metin, Dy, Jennifer G., Bonato, Paolo

“…Quantitative assessment of motor abilities in stroke survivors can provide valuable feedback to guide clinical interventions. Numerous clinical scales were…”
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A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning by Vecerik, Mel, Sushkov, Oleg, Barker, David, Rothorl, Thomas, Hester, Todd, Scholz, Jon

“…Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing. Existing approaches in the model-based…”
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A Novel Approach to Monitor Rehabilitation Outcomes in Stroke Survivors Using Wearable Technology: ADVANCES IN NEURAL & COGNITIVE ENGINEERING by PATEL, Shyamal, HUGHES, Richard, HESTER, Todd, STEIN, Joel, AKAY, Metin, DY, Jennifer G, BONATO, Paolo

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Challenges of Real-World Reinforcement Learning by Dulac-Arnold, Gabriel, Mankowitz, Daniel, Hester, Todd

“…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,…”
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Generalized model learning for Reinforcement Learning on a humanoid robot by Hester, Todd, Quinlan, Michael, Stone, Peter

“…Reinforcement learning (RL) algorithms have long been promising methods for enabling an autonomous robot to improve its behavior on sequential decision-making…”
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Processing Wearable Sensor Data to Optimize Deep-Brain Stimulation by Patel, Shyamal, Hester, Todd, Hughes, Richard, Huggins, Nancy, Flaherty, Alice, Standaert, David, Growdon, John, Bonato, Paolo

“…Our study suggests that a sensor-based technique might be an important adjunct to existing clinical measures to improve the management of patients undergoing…”
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RTMBA: A Real-Time Model-Based Reinforcement Learning Architecture for robot control by Hester, T., Quinlan, M., Stone, P.

“…Reinforcement Learning (RL) is a paradigm for learning decision-making tasks that could enable robots to learn and adapt to their situation on-line. For an RL…”
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An empirical investigation of the challenges of real-world reinforcement learning by Dulac-Arnold, Gabriel, Levine, Nir, Mankowitz, Daniel J, Li, Jerry, Paduraru, Cosmin, Gowal, Sven, Hester, Todd

“…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,…”
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Intrinsically motivated model learning for a developing curious agent by Hester, T., Stone, P.

“…Reinforcement Learning (RL) agents are typically deployed to learn a specific, concrete task based on a pre-defined reward function. However, in some cases an…”
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Adaptive Lambda Least-Squares Temporal Difference Learning by Mann, Timothy A, Penedones, Hugo, Mannor, Shie, Hester, Todd

“…Temporal Difference learning or TD($\lambda$) is a fundamental algorithm in the field of reinforcement learning. However, setting TD's $\lambda$ parameter,…”
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A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning by Vecerik, Mel, Sushkov, Oleg, Barker, David, Rothörl, Thomas, Hester, Todd, Scholz, Jon

“…Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing. Existing approaches in the model-based…”
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Tracking motor recovery in stroke survivors undergoing rehabilitation using wearable technology by Patel, S, Hughes, R, Hester, T, Stein, J, Akay, M, Dy, J, Bonato, P

“…Quantitative assessment of motor abilities in stroke survivors undergoing rehabilitation can be a valuable feedback to guide the rehabilitation process. The…”
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Safe Exploration in Continuous Action Spaces by Dalal, Gal, Dvijotham, Krishnamurthy, Vecerik, Matej, Hester, Todd, Paduraru, Cosmin, Tassa, Yuval

“…We address the problem of deploying a reinforcement learning (RL) agent on a physical system such as a datacenter cooling unit or robot, where critical…”
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ETA Prediction with Graph Neural Networks in Google Maps by Derrow-Pinion, Austin, She, Jennifer, Wong, David, Lange, Oliver, Hester, Todd, Perez, Luis, Nunkesser, Marc, Lee, Seongjae, Guo, Xueying, Wiltshire, Brett, Battaglia, Peter W, Gupta, Vishal, Li, Ang, Xu, Zhongwen, Sanchez-Gonzalez, Alvaro, Li, Yujia, Veličković, Petar

“…Travel-time prediction constitutes a task of high importance in transportation networks, with web mapping services like Google Maps regularly serving vast…”
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Robust Reinforcement Learning for Continuous Control with Model Misspecification by Mankowitz, Daniel J, Levine, Nir, Jeong, Rae, Shi, Yuanyuan, Kay, Jackie, Abdolmaleki, Abbas, Springenberg, Jost Tobias, Mann, Timothy, Hester, Todd, Riedmiller, Martin

“…We provide a framework for incorporating robustness -- to perturbations in the transition dynamics which we refer to as model misspecification -- into…”
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Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards by Vecerik, Mel, Hester, Todd, Scholz, Jonathan, Wang, Fumin, Pietquin, Olivier, Piot, Bilal, Heess, Nicolas, Rothörl, Thomas, Lampe, Thomas, Riedmiller, Martin

“…We propose a general and model-free approach for Reinforcement Learning (RL) on real robotics with sparse rewards. We build upon the Deep Deterministic Policy…”
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