Adaptive Human–Machine Evaluation Framework Using Stochastic Gradient Descent-Based Reinforcement Learning for Dynamic Competing Network

Abstract

Complex problems require considerable work, extensive computation, and the development of effective solution methods. Recently, physical hardware- and software-based technologies have been utilized to support problem solving with computers. However, problem solving often involves human expertise and guidance. In these cases, accurate human evaluations and diagnoses must be communicated to the system, which should be done using a series of real numbers. In previous studies, only binary numbers have been used for this purpose. Hence, to achieve this objective, this paper proposes a new method of learning complex network topologies that coexist and compete in the same environment and interfere with the learning objectives of the others. Considering the special problem of reinforcement learning in an environment in which multiple network topologies coexist, we propose a policy that properly computes and updates the rewards derived from quantitative human evaluation and computes together with the rewards of the system. The rewards derived from the quantitative human evaluation are designed to be updated quickly and easily in an adaptive manner. Our new framework was applied to a basketball game for validation and demonstrated greater effectiveness than the existing methods.

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

“…From the perspective of sports cognition, the decision-making of basketball players directly affects the tactical performance and game result of the whole team (Žemgulys et al, 2018 ; Kim and Lee, 2020 ). Therefore, players are required to be able to capture the basketball target in real time and complete relevant information processing when make decisions.…”

Cooperative Multi-Agent Interaction and Evaluation Framework Considering Competitive Networks with Dynamic Topology Changes

“…This is an example of a special case that differs from those previously investigated. In this paper, to deal with the environment in which two or more dynamically changing multi-agents in networks compete and coexist with each other, designed an artificial intelligence soccer game similar to the artificial intelligence basketball game covered in [27]. deep neural networks to learn the environment and make decisions regarding the allocation problems according to network conditions, such as service latency and requirements [31].…”

“…Existing studies to effectively learn through human evaluation mainly deal with simple, uncomplicated problems of a single network. In a recent study dealing with the situation where two or more network topologies compete and coexist, an effective algorithm is proposed in which human evaluation is adaptively updated [27]. In this paper, propose an algorithm that strategically updates rewards until a stable situation is reached, dealing with an environment where two or more dynamically changing networks compete with each other and adaptively update human evaluation.…”

A Novel Hierarchical Soft Actor-Critic Algorithm for Multi-Logistics Robots Task Allocation

“…The achievements of the research on multi-agent dynamic task allocation [30][31][32] are mainly based on heuristic intelligent algorithms. Intelligent algorithms mainly use environmental learning or heuristic search, such as A* algorithms [33], evolutionary algorithms [34][35][36], and neural network-based methods, etc.…”