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-oriented reaching tasks performed using a planar musculoskeletal human arm simulation.  The RL controller learning achieved using human trainers was compared with learning accomplished using human-like rewards generated by an algorithm; metrics included success at reaching the specified target; time required to reach the target; and target overshoot.  These results suggest that human rewards can be used effectively to train RL-based FES controllers.
RL
Training an Actor-Critic Reinforcement Learning Controller for Arm Movement Using Human-Generated Rewards.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.  These results suggest that human rewards can be used effectively to train RL-based FES controllers.
FES