Invention Grant
- Patent Title: Hierarchical and interpretable skill acquisition in multi-task reinforcement learning
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Application No.: US15885727Application Date: 2018-01-31
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Publication No.: US11562287B2Publication Date: 2023-01-24
- Inventor: Caiming Xiong , Tianmin Shu , Richard Socher
- Applicant: salesforce.com, inc.
- Applicant Address: US CA San Francisco
- Assignee: salesforce.com, inc.
- Current Assignee: salesforce.com, inc.
- Current Assignee Address: US CA San Francisco
- Agency: Haynes and Boone LLP
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06N3/08 ; G06F9/48 ; G06N3/04

Abstract:
The disclosed technology reveals a hierarchical policy network, for use by a software agent, to accomplish an objective that requires execution of multiple tasks. A terminal policy learned by training the agent on a terminal task set, serves as a base task set of the intermediate task set. An intermediate policy learned by training the agent on an intermediate task set serves as a base policy of the top policy. A top policy learned by training the agent on a top task set serves as a base task set of the top task set. The agent is configurable to accomplish the objective by traversal of the hierarchical policy network. A current task in a current task set is executed by executing a previously-learned task selected from a corresponding base task set governed by a corresponding base policy, or performing a primitive action selected from a library of primitive actions.
Public/Granted literature
- US20190130312A1 HIERARCHICAL AND INTERPRETABLE SKILL ACQUISITION IN MULTI-TASK REINFORCEMENT LEARNING Public/Granted day:2019-05-02
Information query