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Game AI controlled by UCT which achieves excellent performance in computer go can be applied to control non-player characters (NPCs) in video games. While, it is computation intensive algorithm, so applying it to on-line game is not suitable. But data collected from NPC controlled by UCT is able to be utilized to train neuro-controler. Furthermore, neuro-controler is an efficient algorithm due to...
Reinforcement learning (RL) is a popular learning paradigm to adaptive learning control of nonlinear systems, and is able to work without an explicit model. However, learning from scratch, i.e., without any a priori knowledge, is a daunting undertaking, which results in long training time and instability of learning process with large continuous state space. For physical systems, one must consider...
Levenberg-Marquardt (LM) algorithm, a powerful off-line batch training method for neural networks, is adapted here for online estimation of power system dynamic behavior. A special form of neural network compatible with the feedback linearization framework is used to enable non-linear self-tuning control. Use of LM is shown to yield better closed-loop performance compared to conventional recursive...
In case-based reasoning (CBR), cases are generally represented by features. Different features have different importance, which are often described by weights. So how to adaptively learning weights of different features is a very key issue in CBR, which impact directly the quality and performance of case extraction. Currently, in most practical CBR systems, the feature weights are given by domain...
A novel adaptive-critic-based NN controller using reinforcement learning is developed for a class of nonlinear systems with non-symmetric dead-zone inputs. The adaptive critic NN controller uses two NNs: the critic NN is used to approximate the strategic utility function, and the output of action NN is used to approximate the unknown nonlinear function and to minimize the strategic utility function...
This paper studies the problem of adaptive robust iterative learning control for trajectory-tracked task of a class of robotic systems with both structured and unstructured uncertainties. A composite control scheme is proposed in which the periodic uncertainties are approached by the learning controller, while the effect of non-periodic uncertainties on system performances is attenuated by the robust...
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