公开(公告)号：US11334792B2

公开(公告)日：2022-05-17

申请号：US16403388

申请日：2019-05-03

Applicant: DeepMind Technologies Limited

Inventor： Volodymyr Mnih ,  Adria Puigdomenech Badia ,  Alexander Benjamin Graves ,  Timothy James Alexander Harley ,  David Silver ,  Koray Kavukcuoglu

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.

公开(公告)号：US10664753B2

公开(公告)日：2020-05-26

申请号：US16445523

申请日：2019-06-19

Applicant: DeepMind Technologies Limited

Inventor： Benigno Uria-Martínez ,  Alexander Pritzel ,  Charles Blundell ,  Adria Puigdomenech Badia

IPC: G06N3/08 ,  G06N3/00 ,  G06N3/04

Abstract: A method includes maintaining respective episodic memory data for each of multiple actions; receiving a current observation characterizing a current state of an environment being interacted with by an agent; processing the current observation using an embedding neural network in accordance with current values of parameters of the embedding neural network to generate a current key embedding for the current observation; for each action of the plurality of actions: determining the p nearest key embeddings in the episodic memory data for the action to the current key embedding according to a distance measure, and determining a Q value for the action from the return estimates mapped to by the p nearest key embeddings in the episodic memory data for the action; and selecting, using the Q values for the actions, an action from the multiple actions as the action to be performed by the agent.

公开(公告)号：US20190303764A1

公开(公告)日：2019-10-03

申请号：US16445523

申请日：2019-06-19

Applicant: DeepMind Technologies Limited

Inventor： Benigno Uria-Martínez ,  Alexander Pritzel ,  Charles Blundell ,  Adria Puigdomenech Badia

IPC: G06N3/08

Abstract: A method includes maintaining respective episodic memory data for each of multiple actions; receiving a current observation characterizing a current state of an environment being interacted with by an agent; processing the current observation using an embedding neural network in accordance with current values of parameters of the embedding neural network to generate a current key embedding for the current observation; for each action of the plurality of actions: determining the p nearest key embeddings in the episodic memory data for the action to the current key embedding according to a distance measure, and determining a Q value for the action from the return estimates mapped to by the p nearest key embeddings in the episodic memory data for the action; and selecting, using the Q values for the actions, an action from the multiple actions as the action to be performed by the agent.

公开(公告)号：US20190258929A1

公开(公告)日：2019-08-22

申请号：US16403388

申请日：2019-05-03

Applicant: DeepMind Technologies Limited

Inventor： Volodymyr Mnih ,  Adria Puigdomenech Badia ,  Alexander Benjamin Graves ,  Timothy James Alexander Harley ,  David Silver ,  Koray Kavukcuoglu

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.