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

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Continuous-time ADP for linear systems with partially unknown dynamics

Continuous-time ADP for linear systems with partially unknow...

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ieee International symposium on Approximate dynamic programming and reinforcement learning

作者： Vrabie, Draguna Abu-Khalaf, Murad Lewis, Frank L. Wang, Youyi Univ Texas Automat & Robot Res Inst Ft Worth TX 76118 USA Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781424407064

Approximate dynamic programming has been formulated and applied mainly to discrete-time systems. Expressing the ADP concept for continuous-time systems raises difficult issues related to sampling time and system model knowledge requirements. In this paper is presented a novel online adaptive critic (AC) scheme, based on approximate dynamic programming (ADP), to solve the infinite horizon optimal control problem for continuous-time dynamical systems;thus bringing together concepts from the fields of computational intelligence and control theory. Only partial knowledge about the system model is used, as knowledge about the plant internal dynamics is not needed. The method is thus useful to determine the optimal controller for plants with partially unknown dynamics. It is shown that the proposed iterative ADP algorithm is in fact a Quasi-Newton method to solve the underlying Algebraic Riccati Equation (ARE) of the optimal control problem. An initial gain that determines a stabilizing control policy is not required. In control theory terms, in this paper is developed a direct adaptive control algorithm for obtaining the optimal control solution without knowing the system A matrix.

关键词： approximate dynamic programming adaptive critics policy iterations V-learning

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Agent self-assessment: Determining policy quality without execution

Agent self-assessment: Determining policy quality without ex...

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ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Hans, Alexander Duell, Siegmund Udluft, Steffen Neuroinformatics and Cognitive Robotics Lab Ilmenau University of Technology Ilmenau Germany Machine Learning Group Berlin Institute of Technology Berlin Germany Intelligent Systems and Control Siemens AG Corporate Technology Munich Munich Germany

ISBN: (纸本)9781424498888

With the development of data-efficient reinforcement learning (RL) methods, a promising data-driven solution for optimal control of complex technical systems has become available. For the application of RL to a technical system, it is usually required to evaluate a policy before actually applying it to ensure it operates the system safely and within required performance bounds. In benchmark applications one can use the system dynamics directly to measure the policy quality. In real applications, however, this might be too expensive or even impossible. Being unable to evaluate the policy without using the actual system hinders the application of RL to autonomous controllers. As a first step toward agent self-assessment, we deal with discrete MDPs in this paper. We propose to use the value function along with its uncertainty to assess a policy's quality and show that, when dealing with an MDP estimated from observations, the value function itself can be misleading. We address this problem by determining the value function's uncertainty through uncertainty propagation and evaluate the approach using a number of benchmark applications. © 2011 ieee.

关键词： reinforcement learning

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Policy Gradient Approaches for Multi-Objective Sequential Decision Making: A Comparison

Policy Gradient Approaches for Multi-Objective Sequential De...

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

作者： Parisi, Simone Pirotta, Matteo Smacchia, Nicola Bascetta, Luca Restelli, Marcello Politecn Milan Dept Elect Informat & Bioengn Piazza Leonardo da Vinci 32 I-20133 Milan Italy

ISBN: (纸本)9781479945528

This paper investigates the use of policy gradient techniques to approximate the Pareto frontier in Multi-Objective Markov Decision Processes (MOMDPs). Despite the popularity of policy-gradient algorithms and the fact that gradient-ascent algorithms have been already proposed to numerically solve multi-objective optimization problems, especially in combination with multi-objective evolutionary algorithms, so far little attention has been paid to the use of gradient information to face multi-objective sequential decision problems. Three different Multi-Objective reinforcement-learning (MORL) approaches are here presented. The first two, called radial and Pareto following, start from an initial policy and perform gradient-based policy-search procedures aimed at finding a set of non-dominated policies. Differently, the third approach performs a single gradient-ascent run that, at each step, generates an improved continuous approximation of the Pareto frontier. The parameters of a function that defines a manifold in the policy parameter space are updated following the gradient of some performance criterion so that the sequence of candidate solutions gets as close as possible to the Pareto front. Besides reviewing the three different approaches and discussing their main properties, we empirically compare them with other MORL algorithms on two interesting MOMDPs.

关键词： Pareto optimisation approximation theory decision making evolutionary computation gradient methods learning (artificial intelligence) MOMDPs MORL approaches Pareto following Pareto frontier approximation gradient-ascent algorithms gradient-based policy-search procedures multiobjective Markov decision processes multiobjective evolutionary algorithms multiobjective optimization problems multiobjective reinforcement-learning approaches multiobjective sequential decision making nondominated policies performance criterion policy gradient approaches policy-gradient algorithms radial following Algorithm design and analysis Approximation algorithms Approximation methods Manifolds Measurement Optimization Water resources evolutionary algorithm Performance metrics Pareto optimisation Algorithm design and analysis Manifolds Approximation method gradient methods Approximation Theory Approximation algorithms Water Resources Policies decision making

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Online adaptive learning of optimal control solutions using integral reinforcement learning

Online adaptive learning of optimal control solutions using ...

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作者： Vamvoudakis, Kyriakos G. Vrabie, Draguna Lewis, Frank L. Automation and Robotics Research Institute University of Texas at Arlington Fort Worth TX 76118 United States

ISBN: (纸本)9781424498888

In this paper we introduce an online algorithm that uses integral reinforcement knowledge for learning the continuous-time optimal control solution for nonlinear systems with infinite horizon costs and partial knowledge of the system dynamics. This algorithm is a data based approach to the solution of the Hamilton-Jacobi-Bellman equation and it does not require explicit knowledge on the system's drift dynamics. The adaptive algorithm is based on policy iteration, and it is implemented on an actor/critic structure. Both actor and critic neural networks are adapted simultaneously a persistence of excitation condition is required to guarantee convergence of the critic to the actual optimal value function. Novel tuning algorithms are given for both critic and actor networks, with extra terms in the actor tuning law being required to guarantee closed-loop dynamical stability. The convergence to the optimal controller is proven, and stability of the system is also guaranteed. Simulation examples support the theoretical result. © 2011 ieee.

关键词： adaptive algorithms

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Annealing-Pareto Multi-Objective Multi-Armed Bandit Algorithm

Annealing-Pareto Multi-Objective Multi-Armed Bandit Algorith...

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

作者： Yahyaa, Saba Q. Drugan, Madalina M. Manderick, Bernard Vrije Univ Brussel Dept Comp Sci Pl Laan 2 B-1050 Brussels Belgium

ISBN: (纸本)9781479945528

In the stochastic multi-objective multi-armed bandit (or MOMAB), arms generate a vector of stochastic rewards, one per objective, instead of a single scalar reward. As a result, there is not only one optimal arm, but there is a set of optimal arms (Pareto front) of reward vectors using the Pareto dominance relation and there is a trade-off between finding the optimal arm set (exploration) and selecting fairly or evenly the optimal arms (exploitation). To trade-off between exploration and exploitation, either Pareto knowledge gradient (or Pareto-KG for short), or Pareto upper confidence bound (or Pareto-UCB1 for short) can be used. They combine the KG-policy and UCB1-policy, respectively with the Pareto dominance relation. In this paper, we propose Pareto Thompson sampling that uses Pareto dominance relation to find the Pareto front. We also propose annealing-Pareto algorithm that trades-off between the exploration and exploitation by using a decaying parameter epsilon(t) in combination with Pareto dominance relation. The annealing-Pareto algorithm uses the decaying parameter to explore the Pareto optimal arms and uses Pareto dominance relation to exploit the Pareto front. We experimentally compare Pareto-KG, Pareto-UCB1, Pareto Thompson sampling and the annealing-Pareto algorithms on multi-objective Bernoulli distribution problems and we conclude that the annealing-Pareto is the best performing algorithm.

关键词： Pareto optimisation sampling methods simulated annealing stochastic programming KG-policy MOMAB Pareto Thompson sampling Pareto dominance relation Pareto front Pareto knowledge gradient Pareto optimal arms Pareto upper confidence bound Pareto-KG Pareto-UCB1 UCB1-policy annealing-Pareto multiobjective multiarmed bandit algorithm decaying parameter multiobjective Bernoulli distribution problems multiobjective multiarmed bandit reward vectors stochastic rewards Annealing Entropy Heuristic algorithms Nickel Pareto optimization Probability distribution Vectors Pareto optimisation Heuristic algorithms Probability distribution sampling methods simulated annealing Arm spiral arm entropy Exploration Nickel annealing Arms stochastic programming Stochastic models Cloning Vectors

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Discrete-time nonlinear HJB solution using approximate dynamic programming: Convergence proof

Discrete-time nonlinear HJB solution using approximate dynam...

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ieee International symposium on Approximate dynamic programming and reinforcement learning

作者： Al-Tamimi, Asma Lewis, Frank Univ Texas Automat & Robot Res Inst Ft Worth TX 76118 USA Univ Texas Arlington Automat & Robot Res Inst Ft Worth TX 76118 USA

ISBN: (纸本)9781424407064

In this paper, a greedy iteration scheme based on approximate dynamic programming (ADP), namely Heuristic dynamic programming (HDP), is used to solve for the value function of the Hamilton Jacobi Bellman equation (HJB) that appears in discrete-time (DT) nonlinear optimal control. Two neural networks are used- one to approximate the value function and one to approximate the optimal control action. The importance of ADP is that it allows one to solve the HJB equation for general nonlinear discrete-time systems by using a neural network to approximate the value function. The importance of this paper is that the proof of convergence of the HDP iteration scheme is provided using rigorous methods for general discrete-time nonlinear systems with continuous state and action spaces. Two examples are provided in this paper. The first example is a linear system, where ADP is found to converge to the correct solution of the Algebraic Riccati equation (ARE). The second example considers a nonlinear control system.

关键词： adaptive critics approximate dynamic programming HJB policy iterations.

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A Retrospective on adaptive dynamic programming for Control

A Retrospective on Adaptive Dynamic Programming for Control

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International Joint Conference on Neural Networks

作者： Lendaris, George G. Portland State Univ Syst Sci Grad Program Portland OR 97201 USA

ISBN: (纸本)9781424435494

Some three decades ago, certain computational intelligence methods of reinforcement learning were recognized as implementing an approximation of Bellman's dynamic programming method, which is known in the controls community as an important tool for designing optimal control policies for nonlinear plants and sequential decision making. Significant theoretical and practical developments have occurred within this arena, mostly in the past decade, with the methodology now usually referred to as adaptive dynamic programming (ADP). The objective of this paper is to provide a retrospective of selected threads of such developments. In addition, a commentary is offered concerning present status of ADP, and threads for future research and development within the controls field are suggested.

关键词： reinforcement learning

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Pattern Driven dynamic Scheduling Approach using reinforcement learning

Pattern Driven Dynamic Scheduling Approach using Reinforceme...

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ieee International Conference on Automation and Logistics

作者： Wei Yingzi Jiang Xinli Hao Pingbo Gu Kanfeng Shenyang Ligong Univ Shenyang 110168 Peoples R China Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Peoples R China

ISBN: (纸本)9781424447947

Production scheduling is critical for manufacturing system. Dispatching rules are usually applied dynamically to schedule the job in the dynamic job-shop. The paper presents an adaptive iterative scheduling algorithm that operates dynamically to schedule the job in the dynamic job-shop. In order to get adaptive behavior, the reinforcement learning system is done with the phased Q-learning by defining the intermediate state pattern. We convert the scheduling problem into reinforcement learning problems by constructing a multi-phase dynamic programming process, including the definition of state representation, actions and the reward function. We use five heuristic rules, CNP-CR, CNP-FCFS, CNP-EFT, CNP-EDD and CNP-SPT, as actions and the scheduling objective: minimization of maximum completion time. So a complex dynamic scheduling problem can be divided into a sequential sub-problem easier to solve. We also analyze the time and the solution and present some experimental results.

关键词： reinforcement learning Contract Net Protocol (CNP) State Pattern dynamic Scheduling

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adaptive dynamic programming for Feedback Control

Adaptive Dynamic Programming for Feedback Control

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7th Asian Control Conference (ASCC 2009)

作者： Lewis, Frank L. Vrabie, Draguna Univ Texas Arlington Automat & Robot Res Inst Ft Worth TX 76118 USA

ISBN: (纸本)9781424454402

Living organisms learn by acting on their environment, observing the resulting reward stimulus, and adjusting their actions accordingly to improve the reward. This action-based or reinforcement learning can capture notions of optimal behavior occurring in natural systems. We describe mathematical formulations for reinforcement learning and a practical implementation method known as adaptive dynamic programming. These give us insight into the design of controllers for man-made engineered systems that both learn and exhibit optimal behavior. Relations are show between ADP and adaptive control.

关键词： reinforcement learning

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adaptive computation of optimal nonrandomized policies in constrained average-reward MDPs

Adaptive computation of optimal nonrandomized policies in co...

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

作者： Eugene A. Feinberg Department of Applied Mathematics and Statistics Stony Brook University Stony Brook NY USA

This paper deals with computation of optimal nonrandomized nonstationary policies and mixed stationary policies for average-reward Markov decision processes with multiple criteria and constraints. We consider problems with finite state and action sets satisfying the unichain condition. The described procedure for computing optimal nonrandomized policies can also be used for adaptive control problems.

关键词： Frequency adaptive control Vectors Linear programming Constraint theory Mathematics Statistics State-space methods Probability distribution

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