公开(公告)号：US11256990B2

公开(公告)日：2022-02-22

申请号：US16303101

申请日：2017-05-19

Applicant: DEEPMIND TECHNOLOGIES LIMITED

Inventor： Marc Lanctot ,  Audrunas Gruslys ,  Ivo Danihelka ,  Remi Munos

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a recurrent neural network on training sequences using backpropagation through time. In one aspect, a method includes receiving a training sequence including a respective input at each of a number of time steps; obtaining data defining an amount of memory allocated to storing forward propagation information for use during backpropagation; determining, from the number of time steps in the training sequence and from the amount of memory allocated to storing the forward propagation information, a training policy for processing the training sequence, wherein the training policy defines when to store forward propagation information during forward propagation of the training sequence; and training the recurrent neural network on the training sequence in accordance with the training policy.

公开(公告)号：US11604997B2

公开(公告)日：2023-03-14

申请号：US16603307

申请日：2018-06-11

Applicant: DeepMind Technologies Limited

Inventor： Marc Gendron-Bellemare ,  Mohammad Gheshlaghi Azar ,  Audrunas Gruslys ,  Remi Munos

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/084

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a policy neural network. The policy neural network is used to select actions to be performed by an agent that interacts with an environment by receiving an observation characterizing a state of the environment and performing an action from a set of actions in response to the received observation. A trajectory is obtained from a replay memory, and a final update to current values of the policy network parameters is determined for each training observation in the trajectory. The final updates to the current values of the policy network parameters are determined from selected action updates and leave-one-out updates.

公开(公告)号：US20210110271A1

公开(公告)日：2021-04-15

申请号：US16603307

申请日：2018-06-11

Applicant: DeepMind Technologies Limited

Inventor： Marc Gendron-Bellemare ,  Mohammad Gheshlaghi Azar ,  Audrunas Gruslys ,  Remi Munos

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a policy neural network. The policy neural network is used to select actions to be performed by an agent that interacts with an environment by receiving an observation characterizing a state of the environment and performing an action from a set of actions in response to the received observation. A trajectory is obtained from a replay memory, and a final update to current values of the policy network parameters is determined for each training observation in the trajectory. The final updates to the current values of the policy network parameters are determined from selected action updates and leave-one-out updates.