Performance Objective Extraction of Optimal Controllers: A Hippocampal Learning Approach

Intention inference of autonomous vehicles is crucial to guarantee safety and to mitigate risk. This paper reports a performance objective extraction from expert’s data trajectories for experience transference and to uncover the hidden cost associated to the intent. The algorithm is inspired in the hippocampus learning system for experience exploitation that exhibits the human brain. The hippocampus is responsible of memory and to store past experiences to enable transfer learning and fast convergence.The proposed algorithm extracts, from expert’s data, the performance matrices associated to a hidden utility function using a complementary approach based on an off-policy policy iteration and a matrix extraction inverse reinforcement learning algorithms. Exact performance extraction is obtained by adding a constraint in terms of the measurements of the utility function in a batch-least squares algorithm. Convergence of the proposed approach is verified using Lyapunov recursions. Simulation studies are carried out to demonstrate the effectiveness of the proposed approach.