We adapt reinforcement learning (RL) methods for continuous control to bridge
the gap between complete ignorance and perfect knowledge of the environment.
Our method, Partial Knowledge Least Squares Policy Iteration (PLSPI), takes
inspiration from both model-free RL and model-based control. It uses incomplete
information from a partial model and retains RL's data-driven adaption towards
optimal performance. The linear quadratic regulator provides a case study;
numerical experiments demonstrate the effectiveness and resulting benefits of
the proposed method.Comment: IFAC World Congress 202