Creating a Reinforcement Learning Controller for Functional Electrical Stimulation of a Human Arm.

15 citations recorded by the source corpus.

How researchers see this paper

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

Reinforcement Learning in Healthcare: A Survey (2019, background): “AC-based control strategies [293], [294], [289] were proposed to evaluate target-”
Application of the Actor-Critic Architecture to Functional Electrical Stimulation Control of a Human Arm (2009, methodology): “In prior work by the authors, RL was used to adapt to changing dynamics in a simulated arm, though the resulting controller used impractical exploration, was not stable, and was not shown to be robust to sensor noise (Thomas et al. 2008).”
Safe Reinforcement Learning (2015, methodology): “In this section we discuss our decision to model the environment as a partially observable Markov decision process (POMDP) rather than as a Markov decision process (MDP), even though we are primarily interested in MDPs.”
Training an Actor-Critic Reinforcement Learning Controller for Arm Movement Using Human-Generated Rewards (2017, methodology): “[20], [22] applied RL control to a planar arm simulation, achieving good performance within a few hundred episodes of training.”