Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban Environments

arXiv:2302.04823v4 Announce Type: replace
Abstract: Deriving robust control policies for realistic urban navigation scenarios is not a trivial task. In an end-to-end approach, these policies must map high-dimensional images from the vehicle's cameras to low-level actions such as steering and throttle. While pure Reinforcement Learning (RL) approaches are based exclusively on engineered rewards, Generative Adversarial Imitation Learning (GAIL) agents learn from expert demonstrations while interacting with the environment, which favors GAIL on tasks for which a reward signal is difficult …

abstract adversarial arxiv autonomous autonomous driving cameras control cs.ai cs.lg cs.ro driving environments generative hierarchical images imitation learning low map navigation policies reinforcement reinforcement learning robust type urban