… method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning
technique is used in conjunction with a multilayer neural network model of a fuzzy controller. …

… The main difference between the original Q-learning and Fuzzy Qlearning can be found in …
Fuzzy Q-learning uses fuzzy rules for storing the explored knowledge, while Qlearning keeps …

… This paper proposes a new fuzzy classifier based on reinforcement learning. A fuzzy rule …
basic algorithm), the fuzzy rules compete among themselves in terms of fuzzy reasoning to be a …

… In this paper, n -dimensional patterns are classified using fuzzy rules in the following
manner:(1) R u l e R q : I f x 1 i s A q 1 a n d … a n d x n i s A q n t h e n C l a s s C q w i t h C F q …

… networks and reinforcement learning with human knowledge based on IF–THEN rules. The
… by a single input fuzzy-rules emulated network with the set of IF–THEN rules utilized by the …

… learning can be used in control, then it has been shown to be more efficient than reinforcement
learning … has been a growing interest in reinforcement learning algorithms for fuzzy [8], [9] …

… rule bases depend on the expert's knowledge. To avoid such a criticism, a new navigation
method using fuzzy logic and reinforcement learning is … fuzzy logic and reinforcement learning …

… of a fuzzy rule base constructed and tuned by a human expert. Reinforcement learning
method is capable of learning the fuzzy rules automatically. However, it incurs heavy …

… “supervised learning” with “fuzzy reinforcement learning”, so that we decrease learning
time and the number of failures in RL as well as prevent weaknesses of supervised learning. …

… In experiment one, we start with a set of fuzzy control rules used in Berenji[8] as shown in
Figure 3 and automatically generate a set of four additional agents using initialization values of …