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This is a record of a document published elsewhere, first published by the United States Patent and Trademark Office, as US-12600032-B2, on 14 April 2026. OpenTechnical did not first disclose it. The identifier, hashes and timestamps below record what this library served and when, not when the material became public. View the original.

OT-2026-000000879

Intermediate policy training system and method for agent

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Robotics & Automation › Robot Architectures
Original source
US-12600032-B2, the United States Patent and Trademark Office, 14 April 2026
Document SHA-256
55f33d49a1de11a9886d4134a1e1a48de7c3996dfab093ed4e68cb54976d55bd

Abstract

An intermediate policy training method for agent is provided. This method includes: selecting a source and a target policy from a plurality of policies. Each policy is configured to drive an agent to perform a plurality of actions to be in a plurality of states. Each state includes a plurality of physical properties. The method further includes: respectively selecting one from the source policy and the target policy as a source state and a target state; training an intermediate policy by a reinforcement learning to transition the agent from the source state to the target state over an episode, where the reinforcement learning includes an annealing function for setting a plurality of tolerance boundaries of the plurality of physical properties, and the tolerance boundaries gradually shrink during the episode.

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Citation

OpenTechnical Publication OT-2026-000000879, “Intermediate policy training system and method for agent,” published September 15, 2026 at 19:03:32 UTC, https://opentechnical.org/p/OT-2026-000000879.

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