Frame and method for developing reinforcement learning system

Abstract

The invention relates to a frame and method for developing a reinforcement learning system, which is characterized in that a learner interface interactive with the external environment, a state interface for showing the environment state, an action interface of which the action is executed by the system through an executive component, a basic testing environment and other parts constitute the frame, and then, the frame is used for developing the reinforcement learning system, wherein the learner interface acquires the environment state through the state interface, updates the internal state by learning, makes a decision and calls the action interface to act on the environment. Meanwhile, the study group of the invention also provides the implementation of a new multi-robot reinforcement learning algorithm based on the quantum theory to be used as example demonstration. Developers can finish the development of robots or other intelligent device learning modules only by realizing the corresponding interfaces according to certain steps. The frame of the invention has high portability, can operate in many platforms, can be combined with other robot system frames for use, greatly reduces the compiling complexity of the learning algorithm, and has simple method.

Description

technical field [0001] The invention relates to a framework and method for developing a reinforcement learning system. Background technique [0002] Reinforcement learning, also known as reinforcement learning, is a special and environment-adaptive machine learning method that takes environmental feedback as input. Since the late 1980s, with breakthroughs in the mathematical foundational research of reinforcement learning, the research and application of reinforcement learning has been increasingly carried out, and it has become one of the research hotspots in the field of machine learning. [0003] Reinforcement learning technology can learn the optimal behavior strategy of a dynamic system by perceiving the state of the environment and obtaining uncertain reward values ​​from the environment, and only through similar trial and error methods, thus attracting many researchers. So far, reinforcement learning is immature in many fields, and further research on reinforcement l...

AI Technical Summary

Problems solved by technology

[0005] Traditionally, it is necessary to start from scratch to develop a system based on reinforcement learning. There is no general framework to use, resulting in a lot of duplication of labor, and because there is no standard to follow, it may lead to complex and confusing structures

Images