Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy Lyapunov-based approach to the design of fuzzy controllers
Fuzzy Sets and Systems - Special issue on fuzzy modeling and dynamics
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Fuzzy control rules extraction from perception-based information using computing with words
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Intelligent information systems and applications
Adaptive fuzzy command acquisition with reinforcement learning
IEEE Transactions on Fuzzy Systems
Dynamic balance of a biped robot using fuzzy reinforcement learning agents
Fuzzy Sets and Systems - Special issue: Fuzzy set techniques for intelligent robotic systems
A bio-inspired robotic mechanism for autonomous locomotion in unconventional environments
Autonomous robotic systems
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In this paper, a general fuzzy reinforcement learning (FRL) agent that can utilise not only measurement-based information but also perception-based information by means of computing with words (CW) is proposed. By introducing fuzzy numbers and their arithmetic operations and fuzzy Lyapunov synthesis in the domain of CW, a set of stable fuzzy control rules can be derived from perception-based information. Moreover, based on a neurofuzzy network architecture, the fuzzy rules can be incorporated in the FRL agent to initialise its action network, critic network and evaluation feedback module so as to improve the learning. The performance and applicability of the proposed approach are illustrated through the practical implementation of learning control of an autonomous pole-balancing mobile robot.