Invention Grant
- Patent Title: Reinforcement learning with a stochastic action set
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Application No.: US16578863Application Date: 2019-09-23
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Publication No.: US11615293B2Publication Date: 2023-03-28
- Inventor: Georgios Theocharous , Yash Chandak
- Applicant: ADOBE INC.
- Applicant Address: US CA San Jose
- Assignee: ADOBE INC.
- Current Assignee: ADOBE INC.
- Current Assignee Address: US CA San Jose
- Agency: F. Chau & Associates, LLC
- Main IPC: G06N3/04
- IPC: G06N3/04 ; G06N3/08

Abstract:
Systems and methods are described for a decision-making process including actions characterized by stochastic availability, provide an Markov decision process (MDP) model that includes a stochastic action set based on the decision-making process, compute a policy function for the MDP model using a policy gradient based at least in part on a function representing the stochasticity of the stochastic action set, identify a probability distribution for one or more actions available at a time period using the policy function, and select an action for the time period based on the probability distribution.
Public/Granted literature
- US20210089868A1 REINFORCEMENT LEARNING WITH A STOCHASTIC ACTION SET Public/Granted day:2021-03-25
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