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Partially observable Markov decision processes (POMDPs) provide a rich mathematical model for sequential decision making in partially observable and stochastic environments. Model-free methods use the internal state as a substitute of the belief state which is a sufficient statistic of all past action-observation history in model-based techniques. A main drawback of previous model-free techniques,...
Policy iteration, which evaluates and improves the control policy iteratively, is a reinforcement learning method. Policy evaluation with the least-squares method can draw more useful information from the empirical data and therefore improve the data validity. However, most existing online least-squares policy iteration methods only use each sample just once, resulting in the low utilization rate...
This paper investigates the stabilization problem for networked control systems (NCSs) with limited data rates over an additive white Gaussian noise (AWGN) channel. The notion of control with limited data rates means specifying the lower bound of data rates, above which there exists a coding and control scheme for stabilization of linear time-invariant systems. Different from the literatures, the...
A hierarchical reinforcement learning method based on heuristic reward function is proposed to solve the problem of “curse of dimensionality”, that is the states space will grow exponentially in the number of features, and low convergence speed. The method can reduce state spaces greatly and can enhance the speed of the study. Choose actions with favorable purpose and efficiency so as to optimize...
Based on the differentially perturbed velocity particle swarm optimization, an improved multi-swarm particle swarm optimization (MSPSO) is presented to improve the problem of the slow convergence and diversity loss. The algorithm makes the number of populations search at the same time in the same
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