Cost Inference of Discrete-time Linear Quadratic Control Policies using Human-Behaviour Learning

In this paper, a cost inference algorithm for discrete-time systems using human-behaviour learning is proposed. The approach is inspired in the complementary learning that exhibits the neocortex, hippocampus, and striatum learning systems to achieve complex decision making. The main objective is to infer the hidden cost function from expert's data associated to the hippocampus (off-policy data) and transfer it to the neocortex for policy generalization (on-policy data) in different systems and environments. The neocortex is modelled by a Qlearning and a least-squares identification algorithms for onpolicy learning and system identification. The cost inference is obtained using a one-step gradient descent rule and an inverse optimal control algorithm. Convergence of the cost inference algorithm is discussed using Lyapunov recursions. Simulations verify the effectiveness of the approach.