---
publication_id: "OT-2026-000000879"
title: "Intermediate policy training system and method for agent"
published_at: "2026-09-15T19:03:32.006436Z"
canonical_url: "https://opentechnical.org/p/OT-2026-000000879"
categories:
  - "robotics-automation/robot-architectures"
keywords:
  - "b25j9/163"
  - "b25j9/1605"
  - "b25j9/161"
  - "g05b19/4155"
  - "g05b2219/50391"
---

# Intermediate policy training system and method for agent

## 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.