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

ABSTRACT: Functional Electrical Stimulation (FES) employs neuroprostheses to apply electrical current to the nerves and muscles of individuals paralyzed by spinal cord injury to restore voluntary movement. Neuroprosthesis controllers calculate stimulation patterns to produce desired actions. To date, no existing controller is able to efficiently adapt its control strategy to the wide range of possible physiological arm characteristics, reaching movements, and user preferences that vary over time. Reinforcement learning (RL) is a control strategy that can incorporate human reward signals as inputs to allow human users to shape controller behavior. In this paper, ten neurologically intact human participants assigned subjective numerical rewards to train RL controllers, evaluating animations of goal-or

SUBMITTER: Jagodnik KM 

PROVIDER: S-EPMC7523734 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature