Training an Actor-Critic Reinforcement Learning Controller for Arm Movement Using Human-Generated Rewards

By Kathleen M. Jagodnik, Philip S. Thomas, A. J. van den Bogert, Michael S. Branicky, Robert F. Kirsch. 2017

56 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Chao Yu
2. Jiming Liu
3. Shamim Nemati

In Cognitive Neuroscience

1. Shamim Nemati
2. Chao Yu
3. Jiming Liu

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, background): “ed in deriving an optimal control policy in a variety of healthcare situations, ranging from surgical robot operation [284], [285], [286], [287], [288], functional electrical stimulation (FES) [289], [290], and adaptive rate control for medical video streaming [291], [292]. Li and Burdick [284] appl”
• Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications (2017, methodology): “A reinforcement learning based neuroprosthesis controller was trained to evaluate target-oriented task performed using planar musculoskeletal human arm [157].”