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ieee symposium on Computational Intelligence and Games

作者： Wender, Stefan Watson, Ian Univ Auckland Dept Comp Sci Auckland 1 New Zealand

ISBN: (纸本)9781424429738

This paper describes the design and implementation of a reinforcement learner based on Q-learning. This adaptive agent is applied to the city placement selection task in the commercial computer game Civilization IV. The city placement selection determines the founding sites for the cities in this turn-based empire building game from the Civilization series. Our aim is the creation of an adaptive machine learning approach for a task which is originally performed by a complex deterministic script. This machine learning approach results in a more challenging and dynamic computer AI. We present the preliminary findings on the performance of our reinforcement learning approach and we make a comparison between the performance of the adaptive agent and the original static game AI. Both the comparison and the performance measurements show encouraging results. Furthermore the behaviour and performance of the learning algorithm are elaborated and ways of extending our work are discussed.

关键词： Site selection

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adaptive critic-based neurofuzzy controller for the steam generator water level

Adaptive critic-based neurofuzzy controller for the steam ge...

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15th International Workshop on Room-Temperature Semiconductor X- and Gamma-Ray Detectors/ 2006 ieee Nuclear Science symposium

作者： Fakhrazari, Amin Boroushaki, Mehrdad Sharif Univ Technol Dept Mech Engn Tehran Iran

In this paper, an adaptive critic-based neurofuzzy controller is presented for water level regulation of nuclear steam generators. The problem has been of great concern for many years as the steam generator is a highly nonlinear system showing inverse response dynamics especially at low operating power levels. Fuzzy critic-based learning is a reinforcement learning method based on dynamic programming. The only information available for the critic agent is the system feedback which is interpreted as the last action the controller has performed in the previous state. The signal produced by the critic agent is used alongside the backpropagation of error algorithm to tune online conclusion parts of the fuzzy inference rules. The critic agent here has a proportional-derivative structure and the fuzzy rule base has nine rules. The proposed controller shows satisfactory transient responses, disturbance rejection and robustness to model uncertainty. Its simple design procedure and structure, nominates it as one of the suitable controller designs for the steam generator water level control in nuclear power plant industry.

关键词： adaptive critic-based design fuzzy logic reinforcement learning vertical U-tube steam generator

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dynamic Pricing by Multiagent reinforcement learning

Dynamic Pricing by Multiagent Reinforcement Learning

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International symposium on Electronic Commerce and Security

作者： Han, Wei Liu, Lingbo Zheng, Huaili Nanjing Univ Finance & Econ Informat Engn Coll Nanjing 210046 Peoples R China

ISBN: (纸本)9780769532585

dynamic pricing in electronic marketplaces is a basic problem in electronic commercial. In multiagent environments, the optimal pricing policy of agent depends on the pricing policies of other agents. This makes the learning problem more problematic. This paper proposes an efficient online learning algorithm, which integrates the observed objective actions as well as the subjective inferential intention of the opponents. by establishing the decision model of other agents and predicting their proposed price in advance, agent becomes adaptive to its opponents and can make good decisions in long terms. The algorithm is proven to be effective when coming to the problem of seller pricing in electronic marketplaces.

关键词： reinforcement learning

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A Biologically-Inspired Computational Model for Transformation Invariant Target Recognition

A Biologically-Inspired Computational Model for Transformati...

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International Joint Conference on Neural Networks

作者： Iftekharuddin, Khan M. Li, Yaqin Univ Memphis Dept Elect & Comp Engn Intelligence Syst & Image Proc Lab Memphis TN 38152 USA

ISBN: (纸本)9781424418206

Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics' medical practices, geographic scene analysis, and many others. One of the primary challenges is detection and recognition of objects in the presence of transformations such as resolution, rotation, translation, scale and occlusion. In this work, we investigate a biologically-inspired computational modeling approach that exploits reinforcement learning (RL) for transformation-invariant image recognition. The RL is implemented in an adaptive critic design (ACD) framework to approximate the neuro-dynamic programming. Two ACD algorithms such as Heuristic dynamic programming (HDP) and Dual Heuristic dynamic programming (DHP) are investigated and compared for transformation invariant recognition. The two learning algorithms are evaluated statistically using simulated transformations in 2-D images as well as with a large-scale UMIST 2-D face database with pose variations. Our simulations show promising results for both HDP and DHP or transformation-invariant image recognition as well as face authentication. Comparing the two algorithms, DHP outperforms HDP in learning capability, as DHP takes fewer steps to perform a successful recognition task in general. On the other hand, HDP is more robust than DHP as far as success rate across the database is concerned when applied in a stochastic and uncertain environment, and the computational complexity involved in HDP is much less.

关键词： reinforcement learning

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adaptive dynamic programming for multi-intersections traffic signal intelligent control

Adaptive dynamic programming for multi-intersections traffic...

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11th International ieee Conference on Intelligent Transportation Systems, ITSC 2008

作者： Li, Tao Zhao, Dongbin Yi, Jianqiang Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences 95 Zhongguancun East Road Haidian District Beijing 100080 China University of Arizona United States

ISBN: (纸本)9781424421121

This paper aims at developing near optimal traffic signal control for multi-intersections in city. As a new optimization technique, adaptive dynamic programming (ADP) combines concepts of reinforcement learning and dynamic programming. ADP could learn continually from experience to achieve a near optimal control policy under varying conditions. However, without the cooperation among adjacent intersections, the near optimal control for each individual intersection can not guarantee a larger traffic area composing several intersections to be near optimal. This paper presents a new signal control method based on a model-free action-dependent ADP (ADHDP). This method can be used for cooperative control of multiple intersections. In every intersection, an ADHDP signal controller is adopted to adjust signal time according to an integrated unity parameter. The unity parameter is designed to consider not only the control performance in local intersection but also those in the neighbor intersections. Thus the designed controllers could achieve a set of near optimal control police for multi-intersections in a long run. Simulation results show that the trained controller achieves shorter average vehicular delay. © 2008 ieee.

关键词： dynamic programming

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Foreword - ADP: The Key Direction for Future Research in Intelligent Control and Understanding Brain Intelligence

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ieee Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 2008年第4期38卷 898-900页

作者： Paul J. Werbos National Science Foundation Arlington VA USA

This forward to the special issue on adaptive dynamic programming (ADP) and reinforcement learning in feedback control is written by Paul Werbos, the founder of ADP.

关键词： Intelligent control Nonlinear equations adaptive control Intelligent systems Maxwell equations Mathematics Psychology dynamic programming Intelligent structures Resonance light scattering

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reinforcement learning of adaptive longitudinal vehicle control for dynamic collaborative driving

Reinforcement learning of adaptive longitudinal vehicle cont...

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ieee symposium on Intelligent Vehicle

作者： Luke Ng Christopher M. Clark Jan P. Huissoon Department Mechanical and Mechatronics Engineering University of Waterloo ONT Canada Computer Science Department California Polytechnic State University San Louis Obispo CA USA Department of Mechanical and Mechatronics Engineering University of Waterloo ONT Canada

dynamic collaborative driving involves the motion coordination of multiple vehicles using shared information from vehicles instrumented to perceive their surroundings in order to improve road usage and safety. A basic requirement of any vehicle participating in dynamic collaborative driving is longitudinal control. Without this capability, higher-level coordination is not possible. This paper focuses on the problem of longitudinal motion control. A detailed nonlinear longitudinal vehicle model which serves as the control system design platform is used to develop a longitudinal adaptive control system based on Monte Carlo reinforcement learning. The results of the reinforcement learning phase and the performance of the adaptive control system for a single automobile as well as the performance in a multi-vehicle platoon is presented.

关键词： Vehicles

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Using reinforcement learning for city site selection in the turn-based strategy game Civilization IV

Using reinforcement learning for city site selection in the ...

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ieee symposium on Computational Intelligence and Games, CIG

作者： Stefan Wender Ian Watson Department of Computer Science University of Auckland Auckland New Zealand

关键词： Cities and towns Artificial intelligence Games Testing Machine learning Machine learning algorithms Buildings Measurement learning systems Computer science

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RL-Based Scheduling Strategies in Actual Grid Environments

RL-Based Scheduling Strategies in Actual Grid Environments

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International symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Bernardo Costa Inês Dutra Marta Mattoso COPPE Sistemas UFRJ Rio de Janeiro Brazil DCC University of Porto Porto Portugal

In this work, we study the behaviour of different resource scheduling strategies when doing job orchestration in grid environments. We empirically demonstrate that scheduling strategies based on reinforcement learning... 详细信息

关键词： dynamic scheduling Resource management Processor scheduling learning Scheduling algorithm Master-slave dynamic programming Distributed processing Grid computing Round robin

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adaptive dynamic programming for Multi-intersections Traffic Signal Intelligent Control

Adaptive Dynamic Programming for Multi-intersections Traffic...

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International Conference on Intelligent Transportation

作者： Tao Li Dongbin Zhao Jianqiang Yi Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy and Sciences Beijing China China Scholarship Council University of Arizona Tucson USA

关键词： Programmable control adaptive control dynamic programming Intelligent control Communication system traffic control Optimal control Traffic control Artificial neural networks learning Intelligent transportation systems

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