Linguistic reward-oriented Takagi-Sugeno fuzzy reinforcement learning

This paper presents a new learning method to attack two significant sub-problems in reinforcement learning at the same time: continuous space and linguistic rewards. Linguistic reward-oriented Takagi-Sugeno fuzzy reinforcement learning (LRTSFRL) is constructed by combining Q-learning with Takagi-Sugeno type fuzzy inference systems. The proposed paradigm is capable of solving complicated learning tasks of continuous domains, also can be used to design Takagi-Sugeno fuzzy logic controllers. Experiments on the double inverted pendulum system demonstrate the performance and applicability of the presented scheme. Finally, the conclusion remark is drawn