公开(公告)号：US11868882B2

公开(公告)日：2024-01-09

申请号：US16624245

申请日：2018-06-28

Applicant: DEEPMIND TECHNOLOGIES LIMITED

Inventor： Olivier Claude Pietquin ,  Martin Riedmiller ,  Wang Fumin ,  Bilal Piot ,  Mel Vecerik ,  Todd Andrew Hester ,  Thomas Rothoerl ,  Thomas Lampe ,  Nicolas Manfred Otto Heess ,  Jonathan Karl Scholz

IPC: G06N3/02 ,  G06N3/08 ,  G06N3/045 ,  G06N3/047

CPC classification number: G06N3/08 ,  G06N3/045 ,  G06N3/047

Abstract: An off-policy reinforcement learning actor-critic neural network system configured to select actions from a continuous action space to be performed by an agent interacting with an environment to perform a task. An observation defines environment state data and reward data. The system has an actor neural network which learns a policy function mapping the state data to action data. A critic neural network learns an action-value (Q) function. A replay buffer stores tuples of the state data, the action data, the reward data and new state data. The replay buffer also includes demonstration transition data comprising a set of the tuples from a demonstration of the task within the environment. The neural network system is configured to train the actor neural network and the critic neural network off-policy using stored tuples from the replay buffer comprising tuples both from operation of the system and from the demonstration transition data.

公开(公告)号：US11886997B2

公开(公告)日：2024-01-30

申请号：US17962008

申请日：2022-10-07

Applicant: DeepMind Technologies Limited

Inventor： Olivier Pietquin ,  Martin Riedmiller ,  Wang Fumin ,  Bilal Piot ,  Mel Vecerik ,  Todd Andrew Hester ,  Thomas Rothoerl ,  Thomas Lampe ,  Nicolas Manfred Otto Heess ,  Jonathan Karl Scholz

IPC: G06N3/08 ,  G06N3/045 ,  G06N3/047

CPC classification number: G06N3/08 ,  G06N3/045 ,  G06N3/047

Abstract: An off-policy reinforcement learning actor-critic neural network system configured to select actions from a continuous action space to be performed by an agent interacting with an environment to perform a task. An observation defines environment state data and reward data. The system has an actor neural network which learns a policy function mapping the state data to action data. A critic neural network learns an action-value (Q) function. A replay buffer stores tuples of the state data, the action data, the reward data and new state data. The replay buffer also includes demonstration transition data comprising a set of the tuples from a demonstration of the task within the environment. The neural network system is configured to train the actor neural network and the critic neural network off-policy using stored tuples from the replay buffer comprising tuples both from operation of the system and from the demonstration transition data.

公开(公告)号：US20230023189A1

公开(公告)日：2023-01-26

申请号：US17962008

申请日：2022-10-07

Applicant: DeepMind Technologies Limited

Inventor： Olivier Pietquin ,  Martin Riedmiller ,  Wang Fumin ,  Bilal Piot ,  Mel Vecerik ,  Todd Andrew Hester ,  Thomas Rothoerl ,  Thomas Lampe ,  Nicolas Manfred Otto Heess ,  Jonathan Karl Scholz

IPC: G06N3/08 ,  G06N3/04

Abstract: An off-policy reinforcement learning actor-critic neural network system configured to select actions from a continuous action space to be performed by an agent interacting with an environment to perform a task. An observation defines environment state data and reward data. The system has an actor neural network which learns a policy function mapping the state data to action data. A critic neural network learns an action-value (Q) function. A replay buffer stores tuples of the state data, the action data, the reward data and new state data. The replay buffer also includes demonstration transition data comprising a set of the tuples from a demonstration of the task within the environment. The neural network system is configured to train the actor neural network and the critic neural network off-policy using stored tuples from the replay buffer comprising tuples both from operation of the system and from the demonstration transition data.

公开(公告)号：US11468321B2

公开(公告)日：2022-10-11

申请号：US16624245

申请日：2018-06-28

Applicant: DEEPMIND TECHNOLOGIES LIMITED

Inventor： Olivier Claude Pietquin ,  Martin Riedmiller ,  Wang Fumin ,  Bilal Piot ,  Mel Vecerik ,  Todd Andrew Hester ,  Thomas Rothoerl ,  Thomas Lampe ,  Nicolas Manfred Otto Heess ,  Jonathan Karl Scholz

IPC: G06N3/02 ,  G06N3/08 ,  G06N3/04

Abstract: An off-policy reinforcement learning actor-critic neural network system configured to select actions from a continuous action space to be performed by an agent interacting with an environment to perform a task. An observation defines environment state data and reward data. The system has an actor neural network which learns a policy function mapping the state data to action data. A critic neural network learns an action-value (Q) function. A replay buffer stores tuples of the state data, the action data, the reward data and new state data. The replay buffer also includes demonstration transition data comprising a set of the tuples from a demonstration of the task within the environment. The neural network system is configured to train the actor neural network and the critic neural network off-policy using stored tuples from the replay buffer comprising tuples both from operation of the system and from the demonstration transition data.

公开(公告)号：US20200151562A1

公开(公告)日：2020-05-14

申请号：US16624245

申请日：2018-06-28

Applicant: DEEPMIND TECHNOLOGIES LIMITED

Inventor： Olivier Pietquin ,  Martin Riedmiller ,  Wang Fumin ,  Bilal Piot ,  Mel Vecerik ,  Todd Andrew Hester ,  Thomas Rothörl ,  Thomas Lampe ,  Nicolas Manfred Otto Heess ,  Jonathan Karl Scholz

IPC: G06N3/08 ,  G06N3/04

Abstract: An off-policy reinforcement learning actor-critic neural network system configured to select actions from a continuous action space to be performed by an agent interacting with an environment to perform a task. An observation defines environment state data and reward data. The system has an actor neural network which learns a policy function mapping the state data to action data. A critic neural network learns an action-value (Q) function. A replay buffer stores tuples of the state data, the action data, the reward data and new state data. The replay buffer also includes demonstration transition data comprising a set of the tuples from a demonstration of the task within the environment. The neural network system is configured to train the actor neural network and the critic neural network off-policy using stored tuples from the replay buffer comprising tuples both from operation of the system and from the demonstration transition data.