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公开(公告)号：US20230419113A1
公开(公告)日：2023-12-28
申请号：US18465916
申请日：2023-09-12
Applicant: Amazon Technologies, Inc.
Inventor： Sahika Genc , Sravan Babu Bodapati , Tao Sun , Sunil Mallya Kasaragod
IPC: G06N3/08 , G06F17/16 , G06F16/904 , G05D1/02
CPC classification number: G06N3/08 , G06F17/16 , G06F16/904 , G05D1/0221 , G05D2201/0213
Abstract: A data source configured to provide a representation of an environment of one or more agents is identified. Using a data set obtained from the data source, a neural network-based reinforcement learning model with one or more attention layers is trained. Importance indicators generated by the attention layers are used to identify actions to be initiated by an agent. A trained version of the model is stored.
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公开(公告)号：US12026598B2
公开(公告)日：2024-07-02
申请号：US18193023
申请日：2023-03-30
Applicant: Amazon Technologies, Inc.
Inventor： Yunzhe Tao , Sahika Genc , Tao Sun , Sunil Mallya Kasaragod
Abstract: A training system may create and train a machine learning model with knowledge transfer. The knowledge transfer may transfer knowledge that is acquired by another machine learning model that has been previously trained to the machine learning model that is under training. The knowledge transfer may include a combination of representation transfer and instance transfer, the two of which may be performed alternatingly. The instance transfer may further include a filter mechanism to selectively identify instances with a satisfactory performance to implement the knowledge transfer.
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公开(公告)号：US11836577B2
公开(公告)日：2023-12-05
申请号：US16201830
申请日：2018-11-27
Applicant: Amazon Technologies, Inc.
Inventor： Sunil Mallya Kasaragod , Sahika Genc , Leo Parker Dirac , Bharathan Balaji , Eric Li Sun , Marthinus Coenraad De Clercq Wentzel
CPC classification number: G06N20/00 , B25J9/163 , B25J9/1605 , B25J9/1671 , G06F7/023 , G06F30/20 , G06F30/27 , G06N5/022
Abstract: A simulation management service receives a request to perform reinforcement learning for a robotic device. The request can include computer-executable code defining a reinforcement function for training a reinforcement learning model for the robotic device. In response to the request, the simulation management service generates a simulation environment and injects the computer-executable code into a simulation application for the robotic device. Using the simulation application and the computer-executable code, the simulation management service performs the reinforcement learning within the simulation environment.
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4.发明授权公开(公告)号：US10824913B1
公开(公告)日：2020-11-03
申请号：US16198313
申请日：2018-11-21
Applicant: Amazon Technologies, Inc.
Inventor： Sahika Genc , Edo Liberty
Abstract: Techniques for performing image-augmentation based simulations on are described. An exemplary embodiment of such performances includes for each tuple of timestamped image and movement data, generating a next image using an image generation neural network based on the timestamped image and movement data, the image being input into the image generation neural network as a non-rendered image, and generating a reward using a reward generating neural network based on the timestamped image and movement data.
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公开(公告)号：US11620576B1
公开(公告)日：2023-04-04
申请号：US16908359
申请日：2020-06-22
Applicant: Amazon Technologies, Inc.
Inventor： Yunzhe Tao , Sahika Genc , Tao Sun , Sunil Mallya Kasaragod
Abstract: A training system may create and train a machine learning model with knowledge transfer. The knowledge transfer may transfer knowledge that is acquired by another machine learning model that has been previously trained to the machine learning model that is under training. The knowledge transfer may include a combination of representation transfer and instance transfer, the two of which may be performed alternatingly. The instance transfer may further include a filter mechanism to selectively identify instances with a satisfactory performance to implement the knowledge transfer.
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Patent Agency Ranking
公开(公告)号：US20200167687A1
公开(公告)日：2020-05-28
申请号：US16201864
申请日：2018-11-27
Applicant: Amazon Technologies, Inc.
Inventor： Sahika Genc , Sunil Mallya Kasaragod , Leo Parker Dirac , Bharathan Balaji , Saurabh Gupta
Abstract: A simulation application container executes a simulation of a system in a simulation environment, through which an agent representing the system uses a reinforcement learning model to operate within the simulation environment. The simulation application container obtains data indicating how the agent performed in the simulation environment and transmits this data to a robot application container. The robot application container uses the data to update the reinforcement learning model and provides the updated reinforcement learning model to perform another iteration of the simulation and continue training the reinforcement learning model.
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公开(公告)号：US12265924B1
公开(公告)日：2025-04-01
申请号：US16908486
申请日：2020-06-22
Applicant: Amazon Technologies, Inc.
Inventor： Tao Sun , Yunzhe Tao , Sahika Genc , Sunil Mallya Kasaragod , Kaiqing Zhang
Abstract: Techniques for robust multi-agent reinforcement learning (MARL) are described. An exemplary method includes initializing a plurality of parameters for a plurality of agents including at least policy parameters and action-value (Q) parameters; performing robust multi-agent reinforcement learning to learn polices for the agents, wherein in the learned polices no agent has an incentive to deviate, the agents include an implicit agent that is to select a worst-case at any given time during the learning process; and at least one agent utilizing its learned policy.
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公开(公告)号：US11900244B1
公开(公告)日：2024-02-13
申请号：US16588789
申请日：2019-09-30
Applicant: Amazon Technologies, Inc.
Inventor： Sahika Genc , Sravan Babu Bodapati , Tao Sun , Sunil Mallya Kasaragod
IPC: G06N3/08 , G05D1/02 , G06F17/16 , G06F16/904
CPC classification number: G06N3/08 , G05D1/0221 , G06F16/904 , G06F17/16 , G05D2201/0213
Abstract: A data source configured to provide a representation of an environment of one or more agents is identified. Using a data set obtained from the data source, a neural network-based reinforcement learning model with one or more attention layers is trained. Importance indicators generated by the attention layers are used to identify actions to be initiated by an agent. A trained version of the model is stored.
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公开(公告)号：US20230252355A1
公开(公告)日：2023-08-10
申请号：US18193023
申请日：2023-03-30
Applicant: Amazon Technologies, Inc.
Inventor： Yunzhe Tao , Sahika Genc , Tao Sun , Sunil Mallya Kasaragod
Abstract: A training system may create and train a machine learning model with knowledge transfer. The knowledge transfer may transfer knowledge that is acquired by another machine learning model that has been previously trained to the machine learning model that is under training. The knowledge transfer may include a combination of representation transfer and instance transfer, the two of which may be performed alternatingly. The instance transfer may further include a filter mechanism to selectively identify instances with a satisfactory performance to implement the knowledge transfer.
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公开(公告)号：US20200167686A1
公开(公告)日：2020-05-28
申请号：US16201830
申请日：2018-11-27
Applicant: Amazon Technologies, Inc.
Inventor： Sunil Mallya Kasaragod , Sahika Genc , Leo Parker Dirac , Bharathan Balaji , Eric Li Sun , Marthinus Coenraad De Clercq Wentzel
Abstract: A simulation management service receives a request to perform reinforcement learning for a robotic device. The request can include computer-executable code defining a reinforcement function for training a reinforcement learning model for the robotic device. In response to the request, the simulation management service generates a simulation environment and injects the computer-executable code into a simulation application for the robotic device. Using the simulation application and the computer-executable code, the simulation management service performs the reinforcement learning within the simulation environment.
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