Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation

Spinal cord injury (SCI) is characterized by damage and resulting dysfunction to the motor, sensory, and/or autonomic nervous systems associated with trauma or disease processes leading to traumatic or non-traumatic SCI, respectively. The functional consequences can therefore be wide-ranging across these systems, with varying degrees of muscle paralysis, sensory impairment, and autonomic dysfunction such as problems with cardiovascular control, thermoregulation, or bowel, bladder, or sexual function [1], [2]. In general, the more rostral (higher) the damage to the spinal cord, the more body systems that will be affected. With respect to motor function, persons with damage to the cervical (neck) area of the spinal cord will have impairments to both lower and upper limb muscles and are diagnosed as having tetraplegia, while persons with damage to the thoracic (back) or lumbar (lower back) area of the spinal cord will have impairments to the muscles of the thorax and/or the lower limbs only and are diagnosed as having paraplegia. Given the functional consequences of SCI are dependent on both the severity and level of the damage to the nervous system, in addition to a variety of other factors such as pre-morbid condition, additional secondary complications, and psychosocial influences, there is a significant degree of heterogeneity in the presentation of persons with SCI [1], [2].