{
  "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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  "title": "Intermediate policy training system and method for agent",
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