Reinforcement learning (RL) has been applied to many fields and applications, but there are
still some dilemmas between exploration and exploitation strategy for action selection policy …

In recent years, multi-agent reinforcement learning techniques have been widely used and
evolved in the field of artificial intelligence. However, traditional reinforcement learning …

Autonomous Vehicles (AVs) promise to disrupt the traditional systems of transportation. An
autonomous driving environment requires an uninterrupted, continuous stream of data and …

This paper addresses a new method for combination of supervised learning and
reinforcement learning (RL). Applying supervised learning in robot navigation encounters …

The interests in “Energy Hub”(EH) and “Smart Grid”(SG) concepts have been increasing, in
recent years. The synergy effect of the coupling between electricity and natural gas grids …

Recently, a continuous reinforcement learning model called fuzzy SARSA (state, action,
reward, state, action) learning (FSL) was proposed for irrigation canals. The main problem …

In this paper, we propose synthesized design of a fuzzy logic controller (FLC) for control of
an underactuated unicycle system. The FLC objective is velocity control of the wheel while …

Agent-based modeling has become a beneficial tool in describing the complex and
intelligent decision-making behaviors of military logistics entities, which is essential in …

In this study, the synchronization problem of the faulty chaotic gyrostat master–slave systems
using a wavelet-based robust adaptive TS fuzzy control is investigated. This control system …

近年来, 多智能体强化学习算法技术已广泛应用于人工智能领域. 系统性地分析了多智能体强化
学习算法, 审视了其在多智能体系统中的应用与进展, 并深入调研了相关研究成果 …