Leveraging conventional control to improve performance of systems using reinforcement learning

While many model-based methods have been proposed for optimal control, it is often difficult to generate model-based optimal controllers for nonlinear systems. One model-free method to solve for optimal control policies is reinforcement learning. Reinforcement learning iteratively trains an agent to optimize a reward function. However, agents often perform poorly at the beginning of training and require a large number of trials to converge to a successful policy. A method is proposed to incorporate domain knowledge of dynamics and control into the controllers using reinforcement learning to reduce the training time needed. Simulations are presented to compare the performance of agents utilizing domain knowledge to those that do not use domain knowledge. The results show that the agents with domain knowledge can accomplish the desired task with less training time than those without domain knowledge.