Application of reinforcement learning to improve control performance of plant

This paper is concerned with the development of an online Reinforcement Learning (RL) technique that significantly improves the control systems behavior. The reinforcement learner is based on Q-learning and the final controller is an artificial neural network whose weights are tuned by on line learning. In order to speed up the learning processes and prevent the plant from the instability, initially a PID is utilized as an augmented controller until the reinforcement learning becomes capable of keep the system stable and prevent the system from undesirable behavior. Example of use is presented and the effectiveness of the proposed approach is shown.