We propose and evaluate an iterative localization mechanism employing Bayesian inference to estimate the position of a target using received signal strength measurements. The probability density functions of the target's coordinates are estimated through a Bayesian network. Herein, we consider an iterative procedure whereby our predictor (posterior distribution) is updated in a sequential order whenever new measurements are made available. The performance of the mechanism is assessed in terms of the respective root mean square error and kernel density estimation of the target coordinates. Our numerical results showed the proposed iterative mechanism achieves increasingly better estimation of the target node position each updating round of the Bayesian network with new input measurements.

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In ordinary reinforcement learning algorithms, a single agent learns to achieve a goal through many episodes. If a learning problem is complicated, it may take much computation time to acquire the optimal policy. Meanwhile, for optimization problems, multi-agent search methods such as particle swarm optimization have been recognized that they are able to find rapidly the global optimal solution for multi-modal functions with wide solution space. We recently proposed swarm reinforcement learning algorithms in which multiple agents learn through not only their respective experiences but also exchanging information among them. In these algorithms, it is important how to design a method of exchanging the information. This paper proposes several methods of exchanging the information. The proposed algorithms using these methods are applied to a shortest path problem, and their performance is compared through numerical experiments.

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