Imitation Learning for Autonomous Parking

Abstract

With the rise of automated systems, there is a growing demand for agents that can make human-like decisions. One of the ways to train such agents is imitation learning (IL), which can converge to the optimal policy faster by referring to expert demonstrations. In this study, IL algorithms were applied as an auxiliary learning method for reinforcement learning algorithm, and the performance of IL algorithms was compared on various expert data. The experiments analyzed the characteristics of the expert data required by each IL algorithm, and the performance di↵erences are clear. In particular, the accuracy of the expert data provided to the Behavioral Cloning algorithm and the diversity of the expert data provided to Generative Adversarial Imitation Learning algorithm were emphasized. These findings contribute practical guidance for training agents through IL.