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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131-140 订阅

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A reinforcement learning approach for sequential mastery testing

A reinforcement learning approach for sequential mastery tes...

引用

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

作者： El-Sayed M. El-Alfy College of Computer Sciences and Engineering King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

This paper explores a novel application for reinforcement learning (RL) techniques to sequential mastery testing. In such systems, the goal is to classify each examined person, using the minimal number of test items, as master or non-master. Using RL, an intelligent agent autonomously learns from interactions to administer more informative and effective variable-length tests. Empirical results are also provided to evaluate the performance of the proposed approach as compared to two common approaches for variable-length testing (Bayesian decision and sequential probability ratio test) as well as to the fixed-length testing.

关键词： Testing Bayesian methods learning Accuracy Decision theory Measurement Adaptation models

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adaptive dynamic programming with balanced weights seeking strategy

Adaptive dynamic programming with balanced weights seeking s...

引用

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

作者： Jian Fu Haibo He Zhen Ni School of Automation Wuhan University of Technology Wuhan Hubei China Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI USA

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.

关键词： Artificial neural networks Equations Jacobian matrices Convergence Algorithm design and analysis Damping Robustness

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Tunable and generic problem instance generation for multi-objective reinforcement learning

Tunable and generic problem instance generation for multi-ob...

引用

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

作者： Deon Garrett Jordi Bieger Kristinn R. Thórisson Icelandic Institute for Intelligent Machines Reykjavík University Iceland

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) Correlation Generators Covariance matrices Benchmark testing Heuristic algorithms Optimization

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Kalman Temporal Differences: The deterministic case

Kalman Temporal Differences: The deterministic case

引用

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

作者： Matthieu Geist Olivier Pietquin Gabriel Fricout IMS Research Group Supélec Metz France IMS Research Group Metz France MC cluster ArcelorMittal Research Maizieres-Les-Metz France

This paper deals with value function and Q-function approximation in deterministic Markovian decision processes. A general statistical framework based on the Kalman filtering paradigm is introduced. Its principle is to adopt a parametric representation of the value function, to model the associated parameter vector as a random variable and to minimize the mean-squared error of the parameters conditioned on past observed transitions. From this general framework, which will be called Kalman Temporal Differences (KTD), and using an approximation scheme called the unscented transform, a family of algorithms is derived, namely KTD-V, KTD-SARSA and KTD-Q, which aim respectively at estimating the value function of a given policy, the Q-function of a given policy and the optimal Q-function. The proposed approach holds for linear and nonlinear parameterization. This framework is discussed and potential advantages and shortcomings are highlighted.

关键词： Kalman filters Equations State-space methods learning Error correction Filtering Random variables Approximation algorithms dynamic programming Vectors

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An adaptive-learning framework for semi-cooperative multi-agent coordination

An adaptive-learning framework for semi-cooperative multi-ag...

引用

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

作者： Abdeslem Boukhtouta Jean Berger Warren B. Powell Abraham George Defence Research and Development Canada QUE Canada Department of Operations Research and Financial Engineering Princeton University Princeton NJ USA

Complex problems involving multiple agents exhibit varying degrees of cooperation. The levels of cooperation might reflect both differences in information as well as differences in goals. In this research, we develop a general mathematical model for distributed, semi-cooperative planning and suggest a solution strategy which involves decomposing the system into subproblems, each of which is specified at a certain period in time and controlled by an agent. The agents communicate marginal values of resources to each other, possibly with distortion. We design experiments to demonstrate the benefits of communication between the agents and show that, with communication, the solution quality approaches that of the ideal situation where the entire problem is controlled by a single agent.

关键词： Mathematical model Multiagent systems Function approximation Equations dynamic programming Approximation algorithms

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A theoretical and empirical analysis of Expected Sarsa

A theoretical and empirical analysis of Expected Sarsa

引用

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

作者： Harm van Seijen Hado van Hasselt Shimon Whiteson Marco Wiering Integrated Systems group TNO Defence Safety and Security The Hague Netherlands Intelligent Systems Group University of Utrecht Utrecht Netherlands Intelligent Autonomous Systems Group University of Amsterdam Amsterdam Netherlands Department of Artificial Intelligence University of Groningam Groningen Netherlands

This paper presents a theoretical and empirical analysis of Expected Sarsa, a variation on Sarsa, the classic on-policy temporal-difference method for model-free reinforcement learning. Expected Sarsa exploits knowledge about stochasticity in the behavior policy to perform updates with lower variance. Doing so allows for higher learning rates and thus faster learning. In deterministic environments, Expected Sarsas updates have zero variance, enabling a learning rate of 1. We prove that Expected Sarsa converges under the same conditions as Sarsa and formulate specific hypotheses about when Expected Sarsa will outperform Sarsa and Q-learning. Experiments in multiple domains confirm these hypotheses and demonstrate that Expected Sarsa has significant advantages over these more commonly used methods.

关键词： Artificial intelligence Convergence Intelligent systems Optimal control Supervised learning Robot control Probability distribution dynamic programming State feedback State estimation

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Model-based multi-objective reinforcement learning

Model-based multi-objective reinforcement learning

引用

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

作者： Marco A. Wiering Maikel Withagen Mădălina M Drugan Institute of Artificial Intelligence University of Groningen The Netherlands Artificial Intelligence Lab Vrije Universiteit Brussel Belgium

This paper describes a novel multi-objective reinforcement learning algorithm. The proposed algorithm first learns a model of the multi-objective sequential decision making problem, after which this learned model is used by a multi-objective dynamic programming method to compute Pareto optimal policies. The advantage of this model-based multi-objective reinforcement learning method is that once an accurate model has been estimated from the experiences of an agent in some environment, the dynamic programming method will compute all Pareto optimal policies. Therefore it is important that the agent explores the environment in an intelligent way by using a good exploration strategy. In this paper we have supplied the agent with two different exploration strategies and compare their effectiveness in estimating accurate models within a reasonable amount of time. The experimental results show that our method with the best exploration strategy is able to quickly learn all Pareto optimal policies for the Deep Sea Treasure problem.

关键词： Computational modeling Pareto optimization learning (artificial intelligence) Heuristic algorithms dynamic programming Vectors Markov processes

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An approximate dynamic programming based controller for an underactuated 6DoF quadrotor

An approximate Dynamic Programming based controller for an u...

引用

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

作者： Emanuel Stingu Frank L. Lewis Automation & Robotics Research Institute University of Texas Arlington Arlington TX USA

This paper discusses how the principles of adaptive dynamic programming (ADP) can be applied to the control of a quadrotor helicopter platform flying in an uncontrolled environment and subjected to various disturbances and model uncertainties. ADP is based on reinforcement learning using an actor-critic structure. Due to the complexity of the quadrotor system, the learning process has to use as much information as possible about the system and the environment. Various methods to improve the learning speed and efficiency are presented. Neural networks with local activation functions are used as function approximators because the state-space can not be explored efficiently due to its size and the limited time available. The complex dynamics is controlled by a single critic and by multiple actors thus avoiding the curse of dimensionality. After a number of iterations, the overall actor-critic structure stores information (knowledge) about the system dynamics and the optimal controller that can accomplish the explicit or implicit goal specified in the cost function.

关键词： Equations Optimal control Rotors Heuristic algorithms Neurons Artificial neural networks Approximation methods

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Using approximate dynamic programming for estimating the revenues of a hydrogen-based high-capacity storage device

Using approximate dynamic programming for estimating the rev...

引用

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

作者： Vincent François-Lavet Raphael Fonteneau Damien Ernst Department of Electrical Engineering and Computer Science University of Liège Belgium

This paper proposes a methodology to estimate the maximum revenue that can be generated by a company that operates a high-capacity storage device to buy or sell electricity on the day-ahead electricity market. The methodology exploits the dynamic programming (DP) principle and is specified for hydrogen-based storage devices that use electrolysis to produce hydrogen and fuel cells to generate electricity from hydrogen. Experimental results are generated using historical data of energy prices on the Belgian market. They show how the storage capacity and other parameters of the storage device influence the optimal revenue. The main conclusion drawn from the experiments is that it may be advisable to invest in large storage tanks to exploit the inter-seasonal price fluctuations of electricity.

关键词： Electricity Hydrogen Fuel cells Electrochemical processes Hydrogen storage dynamic programming

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Neural-network-based adaptive dynamic surface control for MIMO systems with unknown hysteresis

Neural-network-based adaptive dynamic surface control for MI...

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

作者： Lei Liu Zhanshan Wang Zhengwei Shen College of Information Science and Engineering Northeastern University Shenyang Liaoning China

ISBN: (纸本)9781479945511

This paper focuses on the composite adaptive tracking control for a class of nonlinear multiple-input-multiple-output (MIMO) systems with unknown backlash-like hysteresis nonlinearities. A dynamic surface control method is incorporated into the proposed control strategy to eliminate the problem of explosion of complexity. Compared with some existing methods, the prediction error between system state and serial-parallel estimation model is combined with compensated tracking error to construct the adaptive laws for neural network (NN) weights. It is shown that the proposed control approach can guarantee that all the signals of the resulting closed-loop systems are semi-globally uniformly ultimately bounded and the tracking error converges to a small neighborhood. Finally, simulation results are provided to confirm the effectiveness of the proposed approaches.

关键词： Hysteresis Approximation methods adaptive systems MIMO Educational institutions Nonlinear systems Vectors

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