In　this　paper,　we　propose　a　privacy-preserving　reinforcement　learning　framework　for　a　patient-centric　dynamic　treatment　regime,　which　we　refer　to　as　Preyer.　Using　Preyer,　a　patient-centric　treatment　strategy　can　be　made　spontaneously　while　preserving　the　privacy　of　the　patient＇s　current　health　state　and　the　treatment　decision.　Specifically,　we　first　design　a　new　storage　and　computation　method　to　support　noninteger　processing　for　multiple　encrypted　domains.　A　new　secure　plaintext　length　control　protocol　is　also　proposed　to　avoid　plaintext　overflow　after　executing　secure　computation　repeatedly.　Moreover,　we　design　a　new　privacy-preserving　reinforcement　learning　framework　with　experience　replay　to　build　the　model　for　secure　dynamic　treatment　policymaking.　Furthermore,　we　prove　that　Preyer　facilitates　patient　dynamic　treatment　policymaking　without　leaking　sensitive　information　to　unauthorized　parties.　We　also　demonstrate　the　utility　and　efficiency　of　Preyer　using　simulations　and　analysis.