A closed loop Deep Brain Stimulation system comprising: a physiological sensor; an adaptive control system in communication with said physiological sensor; and an implantable pulse generator, said IPG arranged to provide pulses selectively to one or more electrodes of a multi-electrode DBS lead responsive to stimulation parameters provided by said adaptive control system, said adaptive control system comprising: a learning module arranged to determine stimulation parameters to be output to said IPG responsive to feedback received from said physiological sensor wherein said stimulation parameters comprise a selection of a particular set of said one or more electrodes of said multi-electrode DBS lead; and a deterministic algorithmic module arranged to maintain safety limits for determined stimulation parameters provided to said IPG, wherein said adaptive control system is arranged to operate in a plurality of states, the states comprising: a non-adaptive state wherein said adaptive control system controls said IPG to deliver stimulation in accordance with received input stimulation parameters; an adaptive gradient ascent learning state wherein said input stimulation parameters are modified by said adaptive control system and optimal parameters for varying patient conditions are learned responsive to feedback received from said physiological sensor, said learning comprising storing reward function values responsive to said modified stimulation parameters and resultant inputs from said physiological sensor in a Q-learning look up table; and an adaptive QL state in which said adaptive control system operates said IPG alternately responsive to inputs from said physiologic sensor and inputs stored in said QL look up table, said alternate selection responsive to a probabilistic scheme. a physiological sensor; an adaptive control system in communication with said physiological sensor; and an implantable pulse generator, said IPG arranged to provide pulses selectively to one or more electrodes of a multi-electrode DBS lead responsive to stimulation parameters provided by said adaptive control system, a learning module arranged to determine stimulation parameters to be output to said IPG responsive to feedback received from said physiological sensor wherein said stimulation parameters comprise a selection of a particular set of said one or more electrodes of said multi-electrode DBS lead; and a deterministic algorithmic module arranged to maintain safety limits for determined stimulation parameters provided to said IPG, a non-adaptive state wherein said adaptive control system controls said IPG to deliver stimulation in accordance with received input stimulation parameters; an adaptive gradient ascent learning state wherein said input stimulation parameters are modified by said adaptive control system and optimal parameters for varying patient conditions are learned responsive to feedback received from said physiological sensor, said learning comprising storing reward function values responsive to said modified stimulation parameters and resultant inputs from said physiological sensor in a Q-learning look up table; and an adaptive QL state in which said adaptive control system operates said IPG alternately responsive to inputs from said physiologic sensor and inputs stored in said QL look up table, said alternate selection responsive to a probabilistic scheme.