A complementary learning approach for expertise transference of human-optimized controllers
Perrusquia A.1
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作者信息
1. School of Aerospace Transport and Manufacturing Cranfield University
折叠
Abstract
? 2021 Elsevier LtdIn this paper, a complementary learning scheme for experience transference of unknown continuous-time linear systems is proposed. The algorithm is inspired in the complementary learning properties that exhibit the hippocampus and neocortex learning systems via the striatum. The hippocampus is modelled as pattern-separated data of a human optimized controller. The neocortex is modelled as a Q-reinforcement learning algorithm which improves the hippocampus control policy. The complementary learning (striatum) is designed as an inverse reinforcement learning algorithm which relates the hippocampus and neocortex learning models to seek and transfer the weights of the hidden expert's utility function. Convergence of the proposed approach is analysed using Lyapunov recursions. Simulations are given to verify the proposed approach.
Key words
Batch least squares/Complementary learning/Gradient-descent rule/Hippocampus and neocortex learning systems/Inverse reinforcement learning/Q-learning
NSTL
Elsevier