Provided is a learning method for an agent that combines an optimum action exceeding human capability and a human-like behavior which are acquired by reinforcement learning. Play data by a human expert and state data SR and action data AR in the recording of play of the agent of an imitation learning or a reinforcement learning model are inputted. The state data SR is inputted to the learning executor of a blended model of the reinforcement and imitation learning models and the action data AF of the blended model is outputted. A first loss error between the action data AR of the human expert or the agent of the imitation learning model and the action data AF is calculated. A second loss error between the action data ARL outputted on the basis of the state data SR by the executor of the reinforcement learning model or an optimum action algorithm and the action data AF is calculated. A blending error is calculated on the basis of a weight ratio of the first and second loss errors, and the learning executor parameter of the blended model is updated on the basis of the blending error.
LEARNING METHOD, LEARNING DEVICE, AND LEARNING PROGRAM FOR AI AGENT THAT BEHAVES LIKE HUMAN