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 lea...

A new method to approximate the true value in reinforcement learning by using deep neural network is proposed. We simulated the Pacman by using this method. Keywords—reinforcement learning; deep learning; Q-learning;

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...

This paper is about representation in RL. We discuss some of the concepts in representation and generalization in reinforcement learning and argue for higher-order representations, instead of the commonly used propositional representations. The paper contains a small review of current reinforcement learning systems using higher-order representations, followed by a brief discussion. The paper en...

Learning Classifier Systems use reinforcement learning, evolutionary computing and/or heuristics to develop adaptive systems. This paper extends the ZCS Learning Classifier System to improve its internal modelling capabilities. Initially, results are presented which show performance in a traditional reinforcement learning task incorporating lookahead within the rule structure. Then a mechanism ...

Reinforcement learning is suitable for navigation of a mobile robot due to its learning ability without supervised information. Reinforcement learning, however, has difficulties. One is its slow learning, and the other is the necessity of specifying its parameter values without prior information. We proposed to introduce sensory signals into reinforcement learning to improve its learning perfor...

Pursuit Reinforcement guided Competitive Learning: PRCL based on relatively fast online clustering that allows grouping the data in concern into several clusters when the number of data and distribution of data are varied of reinforcement guided competitive learning is proposed. One of applications of the proposed method is image portion retrievals from the relatively large scale of the images ...

This paper presents a hybrid method for learning a dynamic strategy for a robot soccer team. In this method, an imitation learning scheme based on observed robot soccer games is used as a seed for an experience-guided learning scheme based on reinforcement learning. A lack in the application of classic reinforcement learning to the robot soccer problem is the high number of states to be analyze...

This paper shows that the distributed representation found in Learning Vector Quantization (LVQ) enables reinforcement learning methods to cope with a large decision search space, defined in terms of equivalence classes of input patterns like those found in the game of Go. In particular, this paper describes S[arsa]LVQ, a novel reinforcement learning algorithm and shows its feasibility for patt...