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The conventional Q-learning algorithm is described by a finite number of discretized states and discretized actions. When the system is represented in continuous domain, this may cause an abrupt transition of action as the state rapidly changes. To avoid this abrupt transition of action, the learning system requires fine-tuned states. However, the learning time significantly increases and the system...
Now, there are some techniques called machine learning, and reinforcement learning is one of the machine learning which often used for actual machine. In this study, we pay attention to the knowledge that does not depend on a reward in reinforcement learning, and we will improve learning efficiency by using it. Furthermore, we aim at letting agent coping with various tasks under environment where...
In this paper, we propose a face detection framework that combines both feature, and skin pixel approaches, while making the framework self adaptive which is important for non controlled environmental conditions. The framework uses skin color information to reduce the search space for faces by localizing the probable skin regions using a mixture of multivariate Gaussians whose parameters are first...
In order to realize intelligent agent such as autonomous mobile robots, reinforcement learning is one of the necessary techniques in behavior control system. However, applying the reinforcement learning to actual sized problem, the ??curse of dimensionality?? problem in partition of sensory states should be avoided maintaining computational efficiency. In multi-agent reinforcement learning, the problem...
Particle filter (PF) is a method for discrete approximation of dynamic and non-Gaussian probability distribution by using numerous particles, and its procedure can execute at high speed and is suitable for on-line applications. However, in conventional methods, a weighted average value or a maximum weighted value of particles is used as a filter output, and information on most particles is disregarded...
In this paper a novel mechanism for acquiring shared symbols in multi-agent cooperative task is introduced. Inspired by human communication, a technique is suggested in which learning the behaviors and learning how to communicate are decomposed. Decomposing the shared symbol acquisition into two separate learning phases not only simplifies the learning algorithm but also it speeds up the process....
Extreme learning machine (ELM) is one of the effective training algorithms for single hidden layer feedforward neural networks (SLFNs), but it often requires a large number of hidden units which makes the trained networks respond slowly to input patterns. Regularized least-squares extreme learning machine (RLS-ELM) is one of the improvements which can overcome this problem. It determines the input...
This paper describes a method for improving the generalization performance of bagging ensemble by means of using Bayesian approach. We examine the Bayesian prediction using bagging leaning machines for regression problems, and show a method to reduce the generalization loss defined by the square error of the prediction for test data. We examine and validate the effectiveness via numerical experiments...
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