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检索条件"任意字段=2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, ADPRL 2009"

共 232 条记录，以下是51-60 订阅

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An approximate dynamic programming strategy for responsive traffic signal control

An approximate dynamic programming strategy for responsive t...

引用

ieee International symposium on Approximate dynamic programming and reinforcement learning

作者： Cai, Chen Univ Coll London Ctr Transport Studies London WC1E 6BT England

ISBN: (纸本)9781424407064

This paper proposes an approximate dynamic programming strategy for responsive traffic signal control. It is the first attempt that optimizes signal control objective dynamically through adaptive approximation of value function. The proposed value function approximation is separable and exogenous factor independent. The algorithm updates the approximated value function progressively in operation, while preserving the structural property of the control problem. The convergence and performance of the algorithm have been tested in a range of experiments. It has been concluded that the new strategy is as good as the best existing control strategies while being efficient and simple in computation. It also has the potential of being extended to multi-phase signal control at isolate junction and to decentralized network operation.

关键词： dynamic programming Traffic control Function approximation Communication system traffic control adaptive control Roads learning Testing Delay Vehicle safety

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Tunable and Generic Problem Instance Generation for Multi-objective reinforcement learning

Tunable and Generic Problem Instance Generation for Multi-ob...

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ieee symposium on adaptive dynamic programming and reinforcement learning (adprl)

作者： Garrett, Deon Bieger, Jordi Throisson, Kristinn R. Reykjavik Univ Iceland Inst Intelligent Machines Reykjavik Iceland Reykjavik Univ Reykjavik Iceland

ISBN: (纸本)9781479945528

A significant problem facing researchers in reinforcement learning, and particularly in multi-objective learning, is the dearth of good benchmarks. In this paper, we present a method and software tool enabling the creation of random problem instances, including multi-objective learning problems, with specific structural properties. This tool, called Merlin (for Multi-objective Environments for reinforcement learning), provides the ability to control these features in predictable ways, thus allowing researchers to begin to build a more detailed understanding about what features of a problem interact with a given learning algorithm to improve or degrade the algorithm's performance. We present this method and tool, and briefly discuss the controls provided by the generator, its supported options, and their implications on the generated benchmark instances.

关键词： learning (artificial intelligence) software tools Merlin learning algorithm multiobjective environments for reinforcement learning multiobjective learning problem multiobjective reinforcement learning problem facing researcher random problem instance software tool structural property Benchmark testing Correlation Covariance matrices Generators Heuristic algorithms learning (artificial intelligence) Optimization Software Tools Neurofibromin 2 Benchmark testing Structural properties dynamos learning algorithms learning (artificial intelligence) Heuristic algorithms variance covariance matrix Covariance matrix

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adaptive Fault Identification for a Class of Nonlinear dynamic Systems

Adaptive Fault Identification for a Class of Nonlinear Dynam...

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ieee symposium on adaptive dynamic programming and reinforcement learning (adprl)

作者： Wu, Li-Bing Ye, Dan Zhao, Xin-Gang Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Univ Sci & Technol Liaoning Coll Sci Anshan 114051 Liaoning Peoples R China Chinese Acad Sci State Key Lab Robot Shenyang 110016 Liaoning Peoples R China Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Liaoning Peoples R China

ISBN: (纸本)9781479945528

This paper is concerned with the diagnosis problem of actuator faults for a class of nonlinear systems. It is assumed that the upper bound of the Lipschtiz constant of the nonlinearity in the faulty system is unknown. Then, a new nonlinear observer for fault diagnosis based on an adaptive estimator is proposed. Moreover, by making use of the designed adaptive observer with on-line update control law without sigma-modification condition to approximate the faulty system, it is proved that the estimate error of the adaptive control parameter, the output observation error and the error between the system fault and the corresponding estimate value are uniformly ultimately bounded via Lyapunov stability analysis. Finally, simulation examples are provided to illustrate the efficiency of the proposed fault identification approach.

关键词： Errors

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Bayesian active learning with basis functions

Bayesian active learning with basis functions

引用

ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Ryzhov, Ilya O. Powell, Warren B. Operations Research and Financial Engineering Princeton University Princeton NJ 08544 United States

ISBN: (纸本)9781424498888

A common technique for dealing with the curse of dimensionality in approximate dynamic programming is to use a parametric value function approximation, where the value of being in a state is assumed to be a linear combination of basis functions. Even with this simplification, we face the exploration/exploitation dilemma: an inaccurate approximation may lead to poor decisions, making it necessary to sometimes explore actions that appear to be suboptimal. We propose a Bayesian strategy for active learning with basis functions, based on the knowledge gradient concept from the optimal learning literature. The new method performs well in numerical experiments conducted on an energy storage problem. © 2011 ieee.

关键词： dynamic programming

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Particle swarm optimized adaptive dynamic programming

Particle swarm optimized adaptive dynamic programming

引用

ieee International symposium on Approximate dynamic programming and reinforcement learning

作者： Dongbin Zhao Jianqiang Yi Liu, Derong Chinese Acad Sci Inst Automat Key Lab Complex Syst & Intelligence Sci Beijing 100080 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

ISBN: (纸本)9781424407064

Particle swarm optimization is used for the training of the action network and critic network of the adaptive dynamic programming approach. The typical structures of the adaptive dynamic programming and particle swarm optimization are adopted for comparison to other learning algorithms such as gradient descent method. Besides simulation on the balancing of a cart pole plant, a more complex plant pendulum robot (pendubot) is tested for the learning performance. Compared to traditional adaptive dynamic programming approaches, the proposed evolutionary learning strategy is verified as faster convergence and higher efficiency. Furthermore, the structure becomes simple because the plant model does not need to be identified beforehand.

关键词： adaptive dynamic programming Particle swarm optimization Pendubot Pole balancing

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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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Heuristics for Multiagent reinforcement learning in Decentralized Decision Problems

Heuristics for Multiagent Reinforcement Learning in Decentra...

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ieee symposium on adaptive dynamic programming and reinforcement learning (adprl)

作者： Allen, Martin W. Hahn, David MacFarland, Douglas C. Univ Wisconsin Dept Comp Sci La Crosse WI 54601 USA

ISBN: (纸本)9781479945528

Decentralized partially observable Markov decision processes (Dec-POMDPs) model cooperative multiagent scenarios, providing a powerful general framework for team-based artificial intelligence. While optimal algorithms exist for Dec-POMDPs, theoretical and empirical results demonstrate that they are impractical for many problems of real interest. We examine the use of reinforcement learning (RL) as a means to generate adequate, if not optimal, joint policies for Dec-POMDPs. It is easily demonstrated (and expected) that single-agent RL produces results of little joint utility. We therefore investigate heuristic methods, based upon the dynamics of the Dec-POMDP formulation, that bias the learning process to produce coordinated action. Empirical tests on a benchmark problem show that these heuristics significantly enhance learning performance, even out-performing a hand-crafted heuristic in cases where the learning process converges quickly.

关键词： Markov processes learning (artificial intelligence) multi-agent systems Dec-POMDP model cooperative multiagent decentralized decision problem heuristic method multiagent reinforcement learning partially observable Markov decision process team-based artificial intelligence Benchmark testing Complexity theory Equations Heuristic algorithms Joints learning (artificial intelligence) Markov chain Benchmark testing Complexity theory Heuristic algorithms Heuristics Joints Joints Multi-agent systems learning (artificial intelligence) heuristic methods learning processes Decentralized

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adaptive dynamic programming with balanced weights seeking strategy

Adaptive dynamic programming with balanced weights seeking s...

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ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Fu, Jian He, Haibo Ni, Zhen School of Automation Wuhan University of Technology Wuhan Hubei 430070 China Department of Electrical Computerand Biomedical Engineering University of Rhode Island Kingston RI 02881 United States

ISBN: (纸本)9781424498888

In this paper we propose to integrate the recursive Levenberg-Marquardt method into the adaptive dynamic programming (ADP) design for improved learning and adaptive control performance. Our key motivation is to consider a balanced weight updating strategy with the consideration of both robustness and convergence during the online learning process. Specifically, a modified recursive Levenberg-Marquardt (LM) method is integrated into both the action network and critic network of the ADP design, and a detailed learning algorithm is proposed to implement this approach. We test the performance of our approach based on the triple link inverted pendulum, a popular benchmark in the community, to demonstrate online learning and control strategy. Experimental results and comparative study under different noise conditions demonstrate the effectiveness of this approach. © 2011 ieee.

关键词： Inverted pendulum

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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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Enhancing the episodic natural actor-critic algorithm by a regularisation term to stabilize learning of control structures

Enhancing the episodic natural actor-critic algorithm by a r...

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ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Witsch, Andreas Reichle, Roland Geihs, Kurt Lange, Sascha Riedmiller, Martin Distributed Systems Group Universität Kassel Germany Machine Learning Lab Albert-Ludwigs-Universität Freiburg Germany

ISBN: (纸本)9781424498888

Incomplete or imprecise models of control systems make it difficult to find an appropriate structure and parameter set for a corresponding control policy. These problems are addressed by reinforcement learning algorithms like policy gradient methods. We describe how to stabilise the policy gradient descent by introducing a regularisation term to enhance the episodic natural actor-critic approach. This allows a more policy independent usage. © 2011 ieee.

关键词： reinforcement learning

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