Improved Bayesian inverse reinforcement learning method based on combined feedback

Abstract

The invention discloses an improved Bayesian inverse reinforcement learning method based on combined feedback, and provides an interactive learning method combining expert feedback and demonstration,in an LfF, an expert evaluates the behavior of a learner and gives feedback with different rewards, so as to improve the strategy of the learner. In the LfD, the Agent attempts to learn its policy byobserving expert demonstration. The research algorithm of the method is divided into three learning stages: learning is carried out from non-optimal demonstration; learning from the feedback; demonstration and feedback learning; to reduce states requiring iteration According to the number of actions, the Agent strategy is improved through iteration of the graphical Bayesian rule to enhance the learned reward function, and the speed of finding the optimal action is increased.

Description

technical field [0001] The invention belongs to the field of machine learning, and relates to the combination of a reverse reinforcement learning algorithm in machine learning and graphical Bayesian theorem, which is used in combination with expert feedback in the reverse reinforcement learning algorithm for interactive learning. Background technique [0002] In recent years, machine learning is a research hotspot in various fields. The purpose of machine learning is to train complex systems, such as autonomous cars and assistive robots, to perform complex tasks in the real world. [0003] Reinforcement learning is the most widely used algorithm in machine learning, but because the reward function of reinforcement learning is artificially set, it is highly subjective. Inverse reinforcement learning can better solve this problem. The Inverse Reinforcement Learning (IRL) problem was first proposed by Russell (1998), and there are already many IRL algorithms. For example, th...

AI Technical Summary

Problems solved by technology

[0004] Traditional inverse reinforcement learning often requires optimal demonstrations in practical applications, but in reality this condition is often not achieved

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