- Patent Title: Distributional reinforcement learning for continuous control tasks
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Application No.: US16759519Application Date: 2018-10-29
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Publication No.: US11481629B2Publication Date: 2022-10-25
- Inventor: David Budden , Matthew William Hoffman , Gabriel Barth-Maron
- Applicant: DeepMind Technologies Limited
- Applicant Address: GB London
- Assignee: DeepMind Technologies Limited
- Current Assignee: DeepMind Technologies Limited
- Current Assignee Address: GB London
- Agency: Fish & Richardson P.C.
- International Application: PCT/EP2018/079526 WO 20181029
- International Announcement: WO2019/081778 WO 20190502
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N3/04

Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network that is used to select actions to be performed by a reinforcement learning agent interacting with an environment. In particular, the actions are selected from a continuous action space and the system trains the action selection neural network jointly with a distribution Q network that is used to update the parameters of the action selection neural network.
Public/Granted literature
- US20200293883A1 DISTRIBUTIONAL REINFORCEMENT LEARNING FOR CONTINUOUS CONTROL TASKS Public/Granted day:2020-09-17
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