Robot behavior learning model based on utility differential network

Abstract

The invention relates to a robot behavior learning model based on a utility differential network, which comprises a utility fitting network unit, a differential signal calculating network unit, a confidence evaluating network unit, an action decision network unit, an action correcting network unit and an action executing unit. The model realizes the offline learning process and the online decision process. The utility fitting network unit calculates and obtains a utility fitting value of a state after action is executed; the differential signal calculating network unit is used for calculatinga differential signal; the confidence evaluating network unit outputs the confidence obtained by calculating to the action correcting network unit; the action decision network unit outputs an action selecting function; and the action correcting network unit corrects the action selecting function by utilizing confidence, calculates a probability value selected by each action and outputs the actionwith largest probability to the action executing unit for executing. The invention can more favorably ensure the completeness of a robot for obtaining environmental knowledge and more favorably ensure the timeliness and effectiveness of robot behavior decision.

Description

technical field [0001] The invention relates to a robot behavior learning model based on a utility difference network, which belongs to one of new applications in the field of artificial intelligence. Background technique [0002] Robot intelligent behavior generally refers to the process in which a robot performs reasoning and decision-making on the basis of perceiving the surrounding environment to achieve behavioral intelligent decision-making. The establishment of an intelligent behavior decision-making model requires the acquisition, representation and reasoning of knowledge, and the ability to automatically evaluate the pros and cons of robot behavior. At present, the cognitive behavioral model based on reinforcement learning technology has advantages in knowledge acquisition, adaptability to decision-making environment, and reusability, making it the first choice for intelligent behavioral modeling. [0003] The reinforcement learning process requires exploration of ...

AI Technical Summary

Problems solved by technology

The robot behavior learning model based on the utility difference network of the present invention solves the problem of limited knowledge acquisition and too strong experience of the general behavior decision-making model, realizes the offline learning process and online decision-making process, and solves the problem of low real-time reasoning process

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