A Study on Multi-Dimensional Fuzzy Q-learning for Intelligent Robots
نویسندگان
چکیده
Reinforcement learning is one of the most important learning methods for intelligent robots working in unknown/uncertain environments. Multi-dimensional fuzzy Q-learning, an extension of the Q-learning method, has been proposed in this study. The proposed method has been applied for an intelligent robot working in a dynamic environment. The rewards from the evaluation functions and the fuzzy Q-values generated by the neural networks (fuzzy Q-net) are expressed in vector forms in order to obtain optimal behaviors for several different purposes. By applying this learning method, evaluation and learning of fuzzy Q-values for the other behaviors can be carried out simultaneously in one trial. We express fuzzy states as the vector of fuzzy sets for input variables of the fuzzy Q-net. The behavior selection algorithm is also proposed in this study. The simulation results show the effectives of the proposed methods for a mobile robot selects optimal behavior in a dynamic environment.
منابع مشابه
Using BELBIC based optimal controller for omni-directional threewheel robots model identified by LOLIMOT
In this paper, an intelligent controller is applied to control omni-directional robots motion. First, the dynamics of the three wheel robots, as a nonlinear plant with considerable uncertainties, is identified using an efficient algorithm of training, named LoLiMoT. Then, an intelligent controller based on brain emotional learning algorithm is applied to the identified model. This emotional l...
متن کاملAn Online Q-learning Based Multi-Agent LFC for a Multi-Area Multi-Source Power System Including Distributed Energy Resources
This paper presents an online two-stage Q-learning based multi-agent (MA) controller for load frequency control (LFC) in an interconnected multi-area multi-source power system integrated with distributed energy resources (DERs). The proposed control strategy consists of two stages. The first stage is employed a PID controller which its parameters are designed using sine cosine optimization (SCO...
متن کاملUser-based Vehicle Route Guidance in Urban Networks Based on Intelligent Multi Agents Systems and the ANT-Q Algorithm
Guiding vehicles to their destination under dynamic traffic conditions is an important topic in the field of Intelligent Transportation Systems (ITS). Nowadays, many complex systems can be controlled by using multi agent systems. Adaptation with the current condition is an important feature of the agents. In this research, formulation of dynamic guidance for vehicles has been investigated based...
متن کاملA TS Fuzzy Model Derived from a Typical Multi-Layer Perceptron
In this paper, we introduce a Takagi-Sugeno (TS) fuzzy model which is derived from a typical Multi-Layer Perceptron Neural Network (MLP NN). At first, it is shown that the considered MLP NN can be interpreted as a variety of TS fuzzy model. It is discussed that the utilized Membership Function (MF) in such TS fuzzy model, despite its flexible structure, has some major restrictions. After modify...
متن کاملCooperation of Autonomous Mobile Robots Using an Intelligent Integrated Approach
In this work, we developed a hybrid-learning based model for intelligent control of autonomous mobile robots. In the proposed model, we used the Q-learning technique for navigation and the inter-process communication (IPC) for message passing between the robots. First, we developed a simulation program based on multi-agent paradigm that adopts these techniques for controlling the movements of t...
متن کامل