Robot assisted post-stroke rehabilitation training is an effective approach in delivering the highly intensive repetitive training, aiming to retrain the neural pathways in the brain thus to restore and improve the affected mobility skills. The adaptive control of robotic devices, especially assist-as-needed control providing exact assistive force intensity along the intended motion trajectory for fine motion, can be a complex but effective method. A temporal difference based critic-actor reinforcement learning control method is explored in this study. The effectiveness of the method is verified through Matlab simulation and implemented on a hand rehabilitation robotic device. Results suggest that the control system can fulfil the control task with high performance and reliability, thus holding the promise of improving the fine hand motion rehabilitation training efficiency.