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A reinforcement learning (RL) agent needs a fair amount of experience to find a near-optimal policy. Transfer learning has been investigated as a means to reduce the amount of experience required. Transfer learning, however, requires another similar reinforcement learning task as a transfer source, which can also be costly in the amount of experience required. In this research, we examine the possible...
This paper study the multi-objective optimization problem of elevator group control systems by using the Markov Decision Process model. Define the Agent to be the leaner and decision-maker of the MDP model. And then using reinforcement learning Algorithm combined with generic method defines the elements of this model. Moreover we use SARSA(λ) value iteration algorithm which was selected to iterative...
In this paper, a novel approach towards self-generating fuzzy neural network (SGFNN) is proposed. The proposed approach is simple and effective and is able to generate a fuzzy neural network with high accuracy and compact structure. The structure learning algorithm of the proposed SGFNN combines criteria of rule generation with a pruning technology. The Kalman filter (KF) algorithm is used to adjust...
The approximation accuracy of RBF network constructed by the incremental learning algorithm to the target was not high. For function approximation or other requirements of high accuracy, such accuracy of RBF network model can not meet the requirements. We have improved this network model focused on three aspects to improve the bottleneck, and have an experiment and comparatively analyze these improvements...
This paper presents an approximate policy iteration algorithm for solving infinite-horizon, discounted Markov decision processes (MDPs) for which a model of the system is available. The algorithm is similar in spirit to Bellman residual minimization methods. However, by using Gaussian process regression with nondegenerate kernel functions as the underlying cost-to-go function approximation architecture,...
In this paper, a new pruning algorithm based on grey incidence analysis for feedforward neural networks is presented.The pruned network has the optimal topology with avoiding over training and obtaining good generalization.The removed connections and the incorporated connections are chosen according to the degree of grey incidence of each output sequence of the network units. The simulation results...
In this paper, we develop a multi-step prediction algorithm that is guaranteed to converge when using general function approximation. Besides, the new algorithm should satisfy the following requirements: first, it does not have to be faster than TD(0) in the look-up table representation; however, the new algorithm should be faster than residual gradient method. Second, the new algorithm should learn...
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