公开(公告)号：US11663441B2

公开(公告)日：2023-05-30

申请号：US16586437

申请日：2019-09-27

Applicant: DeepMind Technologies Limited

Inventor： Scott Ellison Reed ,  Yusuf Aytar ,  Ziyu Wang ,  Tom Paine ,  Sergio Gomez Colmenarejo ,  David Budden ,  Tobias Pfaff ,  Aaron Gerard Antonius van den Oord ,  Oriol Vinyals ,  Alexander Novikov

IPC: G06N3/006 ,  G06F17/16 ,  G06N3/08 ,  G06F18/22 ,  G06N3/045 ,  G06N3/048 ,  G06V10/764 ,  G06V10/77 ,  G06V10/82

CPC classification number: G06N3/006 ,  G06F17/16 ,  G06F18/22 ,  G06N3/045 ,  G06N3/048 ,  G06N3/08 ,  G06V10/764 ,  G06V10/7715 ,  G06V10/82

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection policy neural network, wherein the action selection policy neural network is configured to process an observation characterizing a state of an environment to generate an action selection policy output, wherein the action selection policy output is used to select an action to be performed by an agent interacting with an environment. In one aspect, a method comprises: obtaining an observation characterizing a state of the environment subsequent to the agent performing a selected action; generating a latent representation of the observation; processing the latent representation of the observation using a discriminator neural network to generate an imitation score; determining a reward from the imitation score; and adjusting the current values of the action selection policy neural network parameters based on the reward using a reinforcement learning training technique.