公开(公告)号：US20220138568A1

公开(公告)日：2022-05-05

申请号：US17453055

申请日：2021-11-01

Applicant: NVIDIA Corporation

Inventor： Nikolai Smolyanskiy ,  Alexey Kamenev ,  Lirui Wang ,  David Nister ,  Ollin Boer Bohan ,  Ishwar Kulkarni ,  Fangkai Yang ,  Julia Ng ,  Alperen Degirmenci ,  Ruchi Bhargava ,  Rotem Aviv

IPC: G06N3/08 ,  G06N3/04

Abstract: In various examples, reinforcement learning is used to train at least one machine learning model (MLM) to control a vehicle by leveraging a deep neural network (DNN) trained on real-world data by using imitation learning to predict movements of one or more actors to define a world model. The DNN may be trained from real-world data to predict attributes of actors, such as locations and/or movements, from input attributes. The predictions may define states of the environment in a simulator, and one or more attributes of one or more actors input into the DNN may be modified or controlled by the simulator to simulate conditions that may otherwise be unfeasible. The MLM(s) may leverage predictions made by the DNN to predict one or more actions for the vehicle.