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

作者： Drugan, Madalina M. Nowe, Ann Manderick, Bernard Vrije Univ Brussel Artificial Intelligence Lab Ixelles Belgium

ISBN: (纸本)9781479945528

Many real-world stochastic environments are inherently multi-objective environments with conflicting objectives. The multi-objective multi-armed bandits (MOMAB) are extensions of the classical, i.e. single objective, multi-armed bandits to reward vectors and multi-objective optimisation techniques are often required to design mechanisms with an efficient exploration / exploitation trade-off. In this paper, we propose the improved Pareto Upper Confidence Bound (iPUCB) algorithm that straightforwardly extends the single objective improved UCB algorithm to reward vectors by deleting the suboptimal arms. The goal of the improved Pareto UCB algorithm, i.e. iPUCB, is to identify the set of best arms, or the Pareto front, in a fixed budget of arm pulls. We experimentally compare the performance of the proposed Pareto upper confidence bound algorithm with the Pareto UCB1 algorithm and the Hoeffding race on a bi-objective example coming from an industrial control applications, i.e. the engagement of wet clutches. We propose a new regret metric based on the Kullback-Leibler divergence to measure the performance of a multi-objective multi-armed bandit algorithm. We show that iPUCB outperforms the other two tested algorithms on the given multi-objective environment.

关键词： Pareto optimisation learning (artificial intelligence) stochastic processes Hoeffding race Kullback-Leibler divergence MOMAB Pareto UCB1 algorithm Pareto upper confidence bounds algorithms UCB algorithm bi-objective example industrial control applications multiobjective environments multiobjective multiarmed bandit algorithm multiobjective multiarmed bandits multiobjective optimisation techniques real-world stochastic environments wet clutches Algorithm design and analysis Electronic mail Hypercubes Measurement Pareto optimization Upper bound Vectors Pareto optimisation wet clutch Algorithm design and analysis electronic mail Hypercube Markov chain Upper bound industrial control Cloning Vectors Stochastic Processes algorithms

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Joining Force of Human Muscular Task Planning With Robot Robust and Delicate Manipulation for programming by Demonstration

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ieee-ASME TRANSACTIONS ON MECHATRONICS 2020年第5期25卷 2574-2584页

作者： Wang, Fei Zhou, Xingqun Wang, Jianhui Zhang, Xing He, Zhenquan Song, Bo Northeastern Univ Fac Robot Sci & Engn Shenyang 110169 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Chinese Acad Sci Inst Intelligent Machines Hefei 230031 Peoples R China

Recently, programing by demonstration (PbD) received much attention for its capacity of fast programming with increasing demands in the robot manipulation area, especially in industrial applications. However, one of the biggest challenges of PbD is the recognition of demonstrator's finger high-fidelity motions especially in the environments with uncertainties, which limits the efficiency and accuracy of PbD. In this article, inspired by human dexterity, a novel PbD approach using the implicit muscular task planning strategy is presented to extract features from the arms' giant movement and the hands' fine motions during the demonstrator's operation. Furthermore, we integrate a deep reinforcement learning control method that further improves the manipulations' adaptive ability in the unknown or dynamic environments. The experimental results show that our proposed approach can deal with relative complex assembly tasks with a success rate of more than 67% within a fit tolerance of 4.2 mm by one-shot demonstration.

关键词： Task analysis Robot sensing systems Gesture recognition ieee transactions Mechatronics Robotic assembly Implicit muscular task planning multisource information fusion programming by demonstration reinforcement learning

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Enhancing supervisory training signals with environmental reinforcement learning using adaptive dynamic programming and artificial neural networks 15

Enhancing supervisory training signals with environmental re...

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15th ieee International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2016

作者： Melton, Niklas Wunsch, Donald C. Applied Computational Intelligence Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO United States

ISBN: (纸本)9781509038466

A method for hybridizing supervised learning with adaptive dynamic programming was developed to increase the speed, quality, and robustness of on-line neural network learning from an imperfect teacher. reinforcement learning is used to modify and enhance the original supervisory signal before learning occurs. This paper describes the method of hybridization and presents a model problem in which a human supervisor teaches a simulated car to drive around a race track. Simulation results show successful learning and improvements in convergence time, error rate, and stability over either component method alone. © 2016 ieee.

关键词： reinforcement learning

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On Model-Free reinforcement learning of Reduced-order Optimal Control for Singularly Perturbed Systems 57

On Model-Free Reinforcement Learning of Reduced-order Optima...

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57th ieee Conference on Decision and Control (CDC)

作者： Mukherjee, Sayak Bai, He Chakrabortty, Aranya North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA Oklahoma State Univ Sch Mech & Aerosp Engn Stillwater OK 74078 USA

ISBN: (纸本)9781538613955

We propose a model-free reduced-order optimal control design for linear time-invariant singularly perturbed (SP) systems using reinforcement learning (RL). Both the state and input matrices of the plant model are assumed to be completely unknown. The only assumption imposed is that the model admits a similarity transformation that results in a SP representation. We propose a variant of adaptive dynamic programming (ADP) that employs only the slow states of this SP model to learn a reduced-order adaptive optimal controller. The method significantly reduces the learning time, and complexity required for the feedback control by taking advantage of this model reduction. We use approximation theorems from singular perturbation theory to establish sub-optimality of the learned controller, and to guarantee closed-loop stability. We validate our results using two representative examples -one with a standard singularly perturbed dynamics, and the other with clustered multi-agent consensus dynamics. Both examples highlight various implementation details and effectiveness of the proposed approach.

关键词： reinforcement learning adaptive dynamic programming model reduction model free control singular perturbation

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A Neural Architecture to Address reinforcement learning Problems

A Neural Architecture to Address Reinforcement Learning Prob...

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International Joint Conference on Neural Networks (IJCNN)

作者： de Arruda, Rodrigo L. S. Von Zuben, Fernando J. Univ Campinas UNICAMP Sch Elect & Comp Engn FEEC Dept Comp Engn & Ind Automat DCA Lab Bioinformat & Bioinspired Comp LBiC Campinas SP Brazil

ISBN: (纸本)9781424496365

In this paper, the reinforcement learning problem is formulated equivalently to a Markov Decision Process. We address the solution of such problem using a novel adaptive dynamic programming algorithm which is based on a Multi-layer Perceptron Neural Network composed of a parameterized function approximator called Wire-Fitting. Extending such established model, this work makes use of concepts of eligibility to conceive faster learning algorithms. The advantage of the proposed approach is founded on the capability to handle continuous environments and to learn a better policy while following another. Simulation results involving the automatic control of an inverted pendulum are presented to indicate the effectiveness of the proposed algorithm.

关键词： Approximation methods dynamic programming Equations Heuristic algorithms Markov decision process Markov processes Markov processes Mathematical model Monte Carlo methods adaptive dynamic programming automatic control dynamic programming inverted pendulum learning (artificial intelligence) learning algorithm multilayer perceptron neural network multilayer perceptrons neural architecture neural net architecture parameterized function approximator reinforcement learning wire-fitting

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Longitudinal Control of Hypersonic Vehicles Based on Direct Heuristic dynamic programming Using ANFIS

Longitudinal Control of Hypersonic Vehicles Based on Direct ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Luo, Xiong Chen, Yi Si, Jennie Liu, Feng USTB Sch Comp & Commun Engn Beijing 100083 Peoples R China Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA

ISBN: (纸本)9781479914845

Since the launch of the scramjet, recent years have witnessed a growing interest in the study of airbreathing hypersonic vehicles. Due to its strong coupling characteristics, high nonlinearity, and uncertain parameters, the control of hypersonic vehicle becomes a great challenge. To deal with those design issues, we propose an adaptive learning control method based on direct heuristic dynamic programming (direct HDP), which is used to track the angle of attack despite the presence of bounded uncertain parameters. Inspired by the adaptive critic designs, direct HDP is one of the adaptive dynamic programming (ADP) methods, which is a modelfree reinforcement learning algorithm using the online learning scheme to solve dynamic control problems in realistic complex environment. In this paper, this direct HDP method is improved by embedding the fuzzy neural network (FNN) in the controller design to enhance its self-learning ability and robustness. Simulation results are provided to demonstrate the effectiveness of our proposed method.

关键词： Hypersonic vehicles

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adaptive Data Replication Optimization Based on reinforcement learning

Adaptive Data Replication Optimization Based on Reinforcemen...

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ieee symposium Series on Computational Intelligence (ieee SSCI)

作者： Wee, Chee Keong Nayak, Richi eHlth Queensland Business Applicat Technol Serv Digital Applicat Serv Brisbane Qld Australia Queensland Univ Technol Sci & Engn Fac Sch Elect Engn & Comp Sci Brisbane Qld Australia

ISBN: (纸本)9781728125473

Data replication plays an important role in enterprise IT landscapes, where data is shared among multiple IT systems. IT administrators need to tune the replicating software's configuration setting for it to perform at its optimum level. It is a challenge to continue optimizing the software's configuration to keep up with the fluctuating workload in a dynamic business environment. We propose a novel approach of using reinforcement learning with meta-heuristics to create an adaptive optimization method for data replication software. The experimental results show the replicating software managed by the proposed approach can perform at an optimum level despite consistently working under changing workloads.

关键词： Data replication optimization meta-heuristics deep reinforcement learning

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A Boundedness Theoretical Analysis for GrADP Design: A Case Study on Maze Navigation

A Boundedness Theoretical Analysis for GrADP Design: A Case ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ni, Zhen Zhong, Xiangnan He, Haibo Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA

ISBN: (纸本)9781479919598

A new theoretical analysis towards the goal representation adaptive dynamic programming (GrADP) design proposed in [1], [2] is investigated in this paper. Unlike the proofs of convergence for adaptive dynamic programming (ADP) in literature, here we provide a new insight for the error bound between the estimated value function and the expected value function. Then we employ the critic network in GrADP approach to approximate the Q value function, and use the action network to provide the control policy. The goal network is adopted to provide the internal reinforcement signal for the critic network over time. Finally, we illustrate that the estimated Q value function is close to the expected value function in an arbitrary small bound on the maze navigation example.

关键词： adaptive dynamic programming (ADP) goal representation ADP (GrADP) reinforcement learning theoretical analysis maze navigation

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reinforcement learning-Based Optimal Stabilization for Unknown Nonlinear Systems Subject to Inputs With Uncertain Constraints

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2020年第10期31卷 4330-4340页

作者： Zhao, Bo Liu, Derong Luo, Chaomin Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Mississippi State Univ Dept Elect & Comp Engn Mississippi State MS 39762 USA

This article presents a novel reinforcement learning strategy that addresses an optimal stabilizing problem for unknown nonlinear systems subject to uncertain input constraints. The control algorithm is composed of two parts, i.e., online learning optimal control for the nominal system and feedforward neural networks (NNs) compensation for handling uncertain input constraints, which are considered as the saturation nonlinearities. Integrating the input-output data and recurrent NN, a Luenberger observer is established to approximate the unknown system dynamics. For nominal systems without input constraints, the online learning optimal control policy is derived by solving Hamilton-Jacobi-Bellman equation via a critic NN alone. By transforming the uncertain input constraints to saturation nonlinearities, the uncertain input constraints can be compensated by employing a feedforward NN compensator. The convergence of the closed-loop system is guaranteed to be uniformly ultimately bounded by using the Lyapunov stability analysis. Finally, the effectiveness of the developed stabilization scheme is illustrated by simulation studies.

关键词： Nonlinear systems Optimal control Artificial neural networks Actuators Observers Feedforward systems adaptive dynamic programming (ADP) neural networks (NNs) optimal control reinforcement learning (RL) uncertain input constraints unknown nonlinear systems

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adaptive Alert Management for Balancing Optimal Performance among Distributed CSOCs using reinforcement learning

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ieee TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2020年第1期31卷 16-33页

作者： Shah, Ankit Ganesan, Rajesh Jajodia, Sushil Samarati, Pierangela Cam, Hasan George Mason Univ Ctr Secure Informat Syst Fairfax VA 20030 USA Univ Milan Comp Sci Dept I-20133 Milan Italy US Army Res Lab Adelphi MD 20783 USA

Large organizations typically have Cybersecurity Operations Centers (CSOCs) distributed at multiple locations that are independently managed, and they have their own cybersecurity analyst workforce. Under normal operating conditions, the CSOC locations are ideally staffed such that the alerts generated from the sensors in a work-shift are thoroughly investigated by the scheduled analysts in a timely manner. Unfortunately, when adverse events such as increase in alert arrival rates or alert investigation rates occur, alerts have to wait for a longer duration for analyst investigation, which poses a direct risk to organizations. Hence, our research objective is to mitigate the impact of the adverse events by dynamically and autonomously re-allocating alerts to other location(s) such that the performances of all the CSOC locations remain balanced. This is achieved through the development of a novel centralized adaptive decision support system whose task is to re-allocate alerts from the affected locations to other locations. This re-allocation decision is non-trivial because the following must be determined: (1) timing of a re-allocation decision, (2) number of alerts to be reallocated, and (3) selection of the locations to which the alerts must be distributed. The centralized decision-maker (henceforth referred to as agent) continuously monitors and controls the level of operational effectiveness-LOE (a quantified performance metric) of all the locations. The agent's decision-making framework is based on the principles of stochastic dynamic programming and is solved using reinforcement learning (RL). In the experiments, the RL approach is compared with both rule-based and load balancing strategies. By simulating real-world scenarios, learning the best decisions for the agent, and applying the decisions on sample realizations of the CSOC's daily operation, the results show that the RL agent outperforms both approaches by generating (near-) optimal decisions that mainta

关键词： Distributed cybersecurity operations center (CSOC) centralized alert management level of operational effectiveness reinforcement learning, and adaptive resource allocation

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