Intermediate policy training system and method for agent

An intermediate policy training system for agent, comprising:

• a storage device configured to store a plurality of instructions;
• and
• a processor electrically connected to the storage device to execute the plurality of instructions and cause a plurality of operations, and the plurality of operations comprises:
• selecting a source policy and a target policy from a plurality of policies, wherein each of the plurality of policies is configured to drive an agent to perform a plurality of actions to be in a plurality of states, with each of the plurality of states comprising a plurality of physical properties;
• respectively selecting one of the plurality of states from the source policy and the target policy as a source state and a target state;
• and
• training an intermediate policy by a reinforcement learning to transition the agent from the source state to the target state over an episode, wherein the reinforcement learning includes an annealing function for setting a plurality of tolerance boundaries of the plurality of physical properties, and the plurality of tolerance boundaries gradually shrink during the episode.