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AssigneeSAS Institute Inc.
InventorsAfshin Oroojlooyjadid, Mohammadreza Nazari, Davood Hajinezhad, Jorge Manuel Gomes da Silva
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A computing system trains a reinforcement learning model comprising multiple different attention model components. The reinforcement learning model trains on training data of a first environment (e.g., a first traffic intersection). The reinforcement learning model trains by training a state attention computer model on the training data that weighs each of respective inputs of a respective state. The reinforcement learning model trains by training an action attention computer model that determines a probability of switching from a first action to a second action of the first set of the multiple candidate actions (e.g., changing traffic colors of traffic lights). Alternatively, or additionally, a computing system generates an indication of a selected outcome according to the reinforcement learning model and sends a selection output to the second environment (e.g., a second traffic intersection with more lanes than the first traffic intersection) to implement the selected action in the second environment.
A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, the computer-program product including instructions operable to cause a computing system to:
A computer-implemented method comprising:
A computing device comprising processor and memory, the memory containing instructions executable by the processor wherein the computing device is configured to:
Dependencies are inferred from the claim text.