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检索条件"任意字段=IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning"

共 307 条记录，以下是81-90 订阅

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Higher-level application of Adaptive dynamic programming/reinforcement learning - A next phase for controls and system identification?

Higher-level application of Adaptive Dynamic Programming/Rei...

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ieee symposium on Adaptive dynamic programming and reinforcement learning

作者： Lendaris, George G. Systems Science Graduate Program Portland State University Portland OR United States

ISBN: (纸本)9781424498888

In previous work it was shown that Adaptive-Critic-type approximate dynamic programming could be applied in a higher-level way to create autonomous agents capable of using experience to discern context and select optimal, context-dependent control policies. Early experiments with this approach were based on full a priori knowledge of the system being monitored. The experiments reported in this paper, using small neural networks representing families of mappings, were designed to explore what happens when knowledge of the system is less precise. Results of these experiments show that agents trained with this approach perform well when subject to even large amounts of noise or when employing (slightly) imperfect models. The results also suggest that aspects of this method of context discernment are consistent with our intuition about human learning. The insights gained from these explorations can be used to guide further efforts for developing this approach into a general methodology for solving arbitrary identification and control problems. © 2011 ieee.

关键词： reinforcement learning

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Short-term stock market timing prediction under reinforcement learning schemes

Short-term stock market timing prediction under reinforcemen...

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2007 ieee symposium on approximate dynamic programming and reinforcement learning, ADPRL 2007

作者： Hailin, Li Dagli, Cihan H. Enke, David Department of Engineering Management and Systems Engineering University of Missouri-Rolla Rolla MO 65409-0370 United States

ISBN: (纸本)1424407060

There are fundamental difficulties when only using a supervised learning philosophy to predict financial stock short-term movements. We present a reinforcement-oriented forecasting framework in which the solution is converted from a typical error-based learning approach to a goal-directed matchbased learning method. The real market timing ability in forecasting is addressed as well as traditional goodness-of-fit-based criteria. We develop two applicable hybrid prediction systems by adopting actor-only and actor-critic reinforcement learning, respectively, and compare them to both a supervised-only model and a classical random walk benchmark in forecasting three daily-based stock indices series within a 21-year learning and testing period. The performance of actor-critic-based systems was demonstrated to be superior to that of other alternatives, while the proposed actor-only systems also showed efficacy. © 2007 ieee.

关键词： reinforcement learning

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ieee SSCI 2011: symposium Series on Computational Intelligence - ADPRL 2011: 2011 ieee symposium on Adaptive dynamic programming and reinforcement learning

IEEE SSCI 2011: Symposium Series on Computational Intelligen...

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symposium Series on Computational Intelligence, ieee SSCI2011 - 2011 ieee symposium on Adaptive dynamic programming and reinforcement learning, ADPRL 2011

ISBN: (纸本)9781424498888

The proceedings contain 45 papers. The topics discussed include: active learning for personalizing treatment;active exploration by searching for experiments that falsify the computed control policy;optimistic planning for sparsely stochastic systems;adaptive sample collection using active learning for kernel-based approximate policy iteration;tree-based variable selection for dimensionality reduction of large-scale control systems;high-order local dynamic programming;safe reinforcement learning in high-risk tasks through policy improvement;agent self-assessment: determining policy quality without execution;reinforcement learning algorithms for solving classification problems;reinforcement learning in multidimensional continuous action spaces;grounding subgoals in information transitions;and directed exploration of policy space using support vector classifiers.

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Adaptive dynamic programming for optimal control of unknown nonlinear discrete-time systems

Adaptive dynamic programming for optimal control of unknown ...

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ieee symposium on Adaptive dynamic programming and reinforcement learning

作者： Liu, Derong Wang, Ding Zhao, Dongbin Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781424498888

An intelligent optimal control scheme for unknown nonlinear discrete-time systems with discount factor in the cost function is proposed in this paper. An iterative adaptive dynamic programming (ADP) algorithm via globalized dual heuristic programming (GDHP) technique is developed to obtain the optimal controller with convergence analysis. Three neural networks are used as parametric structures to facilitate the implementation of the iterative algorithm, which will approximate at each iteration the cost function, the optimal control law, and the unknown nonlinear system, respectively. Two simulation examples are provided to verify the effectiveness of the presented optimal control approach. © 2011 ieee.

关键词： Cost functions

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ieee SSCI 2014 - 2014 ieee symposium Series on Computational Intelligence - ADPRL 2014: 2014 ieee symposium on Adaptive dynamic programming and reinforcement learning, Proceedings

IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational...

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2014 ieee symposium on Adaptive dynamic programming and reinforcement learning, ADPRL 2014

ISBN: (纸本)9781479945535

The proceedings contain 42 papers. The topics discussed include: approximate real-time optimal control based on sparse Gaussian process models;subspace identification for predictive state representation by nuclear norm minimization;active learning for classification: an optimistic approach;convergent reinforcement learning control with neural networks and continuous action search;theoretical analysis of a reinforcement learning based switching scheme;an analysis of optimistic, best-first search for minimax sequential decision making;information-theoretic stochastic optimal control via incremental sampling-based algorithms;policy gradient approaches for multi-objective sequential decision making: a comparison;and cognitive control in cognitive dynamic systems: a new way of thinking inspired by the brain.

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Bayesian Sequential Optimal Experimental Design for Linear Regression with reinforcement learning 21

Bayesian Sequential Optimal Experimental Design for Linear R...

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21st ieee international Conference on Machine learning and Applications (ieee ICMLA)

作者： Santosa, Fadil Anderson, Loren Johns Hopkins Univ Dept Appl Math & Stat Baltimore MD 21218 USA Univ Minnesota Twin Cities Sch Math Minneapolis MN USA

ISBN: (纸本)9781665462839

We perform a comparison study on Bayesian sequential optimal experimental design algorithms applied to linear regression in two unknowns. We transform the Bayesian sequential optimal experimental design problem into a reinforcement learning problem to determine the power of deep reinforcement learning algorithms against baselines including batch design, greedy design, dynamic programming, and approximate dynamic programming. Using KL-divergence to measure information gain in the unknown parameters, we construct objectives for each algorithm to maximize information gain. This work showcases novel comparisons between the aforementioned algorithms and provides a new application of reinforcement learning to Bayesian sequential optimal experimental design for inverse problems in linear regression with multiple parameters.

关键词： design optimization Bayes methods regression analysis reinforcement learning mutual information

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approximate Real-Time Optimal Control Based on Sparse Gaussian Process Models

Approximate Real-Time Optimal Control Based on Sparse Gaussi...

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ieee symposium on Adaptive dynamic programming and reinforcement learning (ADPRL)

作者： Boedecker, Joschka Springenberg, Jost Tobias Wuelfing, Jan Riedmiller, Martin Univ Freiburg Dept Comp Sci Machine Learning Lab D-79110 Freiburg Germany

ISBN: (纸本)9781479945528

In this paper we present a fully automated approach to (approximate) optimal control of non-linear systems. Our algorithm jointly learns a non-parametric model of the system dynamics - based on Gaussian Process Regression (GPR) - and performs receding horizon control using an adapted iterative LQR formulation. This results in an extremely data-efficient learning algorithm that can operate under real-time constraints. When combined with an exploration strategy based on GPR variance, our algorithm successfully learns to control two benchmark problems in simulation (two-link manipulator, cart-pole) as well as to swing-up and balance a real cart-pole system. For all considered problems learning from scratch, that is without prior knowledge provided by an expert, succeeds in less than 10 episodes of interaction with the system.

关键词： Gaussian processes learning systems linear quadratic control manipulators nonlinear dynamical systems regression analysis GPR variance Gaussian process regression approximate real-time optimal control cart-pole system data-efficient learning algorithm iterative LQR formulation nonlinear systems receding horizon control sparse Gaussian process models system dynamics nonparametric model two-link manipulator Approximation algorithms Approximation methods Computational modeling Optimal control Optimization Predictive models Trajectory Gaussian processes Optimal control linear quadratic control Nonlinear systems learning systems Approximation method Nonlinear dynamical systems Approximation algorithms Manipulators Computational modeling Prediction models trajectory exploration strategy regression analysis Benchmark testing

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Proceedings of the 2013 ieee symposium on Adaptive dynamic programming and reinforcement learning, ADPRL 2013 - 2013 ieee symposium Series on Computational Intelligence, SSCI 2013

Proceedings of the 2013 IEEE Symposium on Adaptive Dynamic P...

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2013 4th ieee symposium on Adaptive dynamic programming and reinforcement learning, ADPRL 2013

ISBN: (纸本)9781467359252

The proceedings contain 28 papers. The topics discussed include: local stability analysis of high-order recurrent neural networks with multi-step piecewise linear activation functions;finite-horizon optimal control design for uncertain linear discrete-time systems;adaptive optimal control for nonlinear discrete-time systems;optimal control for a class of nonlinear system with controller constraints based on finite-approximation-errors ADP algorithm;finite horizon stochastic optimal control of uncertain linear networked control system;real-time tracking on adaptive critic design with uniformly ultimately bounded condition;a novel approach for constructing basis functions in approximate dynamic programming for feedback control;and a combined hierarchical reinforcement learning based approach for multi-robot cooperative target searching in complex unknown environments.

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dynamic lead time promising

Dynamic lead time promising

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ieee symposium on Adaptive dynamic programming and reinforcement learning

作者： Reindorp, Matthew J. Fu, Michael C. Department of Industrial Engineering and Innovation Sciences Eindhoven University of Technology Netherlands Robert H. Smith School of Business Institute for Systems Research University of Maryland United States

ISBN: (纸本)9781424498888

We consider a make-to-order business that serves customers in multiple priority classes. Orders from customers in higher classes bring greater revenue, but they expect shorter lead times than customers in lower classes. In making lead time promises, the firm must recognize preexisting order commitments, uncertainty over future demand from each class, and the possibility of supply chain disruptions. We model this scenario as a Markov decision problem and use reinforcement learning to determine the firm's lead time policy. In order to achieve tractability on large problems, we utilize a sequential decision-making approach that effectively allows us to eliminate one dimension from the state space of the system. Initial numerical results from the sequential dynamic approach suggest that the resulting policies more closely approximate optimal policies than static optimization approaches. © 2011 ieee.

关键词： reinforcement learning

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Development of reinforcement learning methods in control and decision making in the large scale dynamic game environments

Development of reinforcement learning methods in control and...

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ieee international symposium on Intelligent Control

作者： Orafa, S. Yazdanpanah, M. J. Lucas, C. Rahimikian, A. Ahmadabadi, M. Nili Univ Tehran Control & Intelligent Proc Ctr Excellence Fac Elect & Comp Engn Tehran Iran

ISBN: (纸本)9780780397989

In this paper, an analytical comparison is done between dynamic programming and reinforcement learning methods in dynamic two-player games. The emphasis is on the large number of states and actions available for each player and different conflictive optimization objectives of these games that make them complicated in modeling and analysis. Optimization and decision making is done through quantifying a modified Q-learning algorithm. By this method, it is shown that the information processing in large scale-long stage games will take shorter times and will result in lower decision costs whereas dynamic programming methods cannot handle them across long time-horizons.

关键词： reinforcement learning

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