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检索条件"任意字段=2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, ADPRL 2014"

共 247 条记录，以下是71-80 订阅

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Approximate reinforcement learning: An overview

Approximate reinforcement learning: An overview

引用

ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Buşoniu, Lucian Ernst, Damien De Schutter, Bart Babuška, Robert Delft Center for Systems and Control Delft Univ. of Technology Netherlands Research Associate of the FRS-FNRS Systems and Modeling Unit University of Liège Liège Belgium

ISBN: (纸本)9781424498888

reinforcement learning (RL) allows agents to learn how to optimally interact with complex environments. Fueled by recent advances in approximation-based algorithms, RL has obtained impressive successes in robotics, artificial intelligence, control, operations research, etc. However, the scarcity of survey papers about approximate RL makes it difficult for newcomers to grasp this intricate field. With the present overview, we take a step toward alleviating this situation. We review methods for approximate RL, starting from their dynamic programming roots and organizing them into three major classes: approximate value iteration, policy iteration, and policy search. Each class is subdivided into representative categories, highlighting among others offline and online algorithms, policy gradient methods, and simulation-based techniques. We also compare the different categories of methods, and outline possible ways to enhance the reviewed algorithms. © 2011 ieee.

关键词： reinforcement learning

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Grounding subgoals in information transitions

Grounding subgoals in information transitions

引用

ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Van Dijk, Sander G. Polani, Daniel Adaptive Systems Research Group University of Hertfordshire Hatfield United Kingdom

ISBN: (纸本)9781424498888

In reinforcement learning problems, the construction of subgoals has been identified as an important step to speed up learning and to enable skill transfer. For this purpose, one typically extracts states from various saliency properties of an MDP transition graph, most notably bottleneck states. Here we introduce an alternative approach to this problem: assuming a family of MDPs with multiple goals but with a fixed transition graph, we introduce the relevant goal information as the amount of Shannon information that the agent needs to maintain about the current goal at a given state to select the appropriate action. We show that there are distinct transition states in the MDP at which new relevant goal information has to be considered for selecting the next action. We argue that these transition states can be interpreted as subgoals for the current task class, and we use these states to automatically create a hierarchical policy, according to the well-established Options model for hierarchical reinforcement learning. © 2011 ieee.

关键词： reinforcement learning

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Discrete-time adaptive dynamic programming using wavelet basis function neural networks

Discrete-time adaptive dynamic programming using wavelet bas...

引用

ieee International symposium on Approximate dynamic programming and reinforcement learning

作者： Jin, Ning Liu, Derong Huang, Ting Pang, Zhongyu Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

ISBN: (纸本)9781424407064

dynamic programming for discrete time systems is difficult due to the "curse of dimensionality": one has to find a series of control actions that must be taken in sequence, hoping that this sequence will lead to the optimal performance cost, but the total cost of those actions will be unknown until the end of that sequence. In this paper, we present our work on adaptive dynamic programming (ADP) for nonlinear discrete time system using neural networks. The neural network we adopted here is the wavelet basis function (WBF) neural network. We will exam the performance of an ADP algorithm using WBF neural networks. The comparison shows that when WBF neural networks are employed, the ADP algorithm gives faster training speed than when PBF neural networks are employed.

关键词： dynamic programming

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Annealing-Pareto Multi-Objective Multi-Armed Bandit Algorithm

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

引用

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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Neuro-controller of Cement Rotary Kiln Temperature with adaptive Critic Designs

Neuro-controller of Cement Rotary Kiln Temperature with Adap...

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ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Lin, Xiaofeng Liu, Tangbo Song, Shaojian Song, Chunning Guangxi Univ Coll Elect Engn Nanning 530004 Peoples R China

ISBN: (纸本)9781424427611

The production process of the cement rotary kiln is a typical engineering thermodynamics with large inertia, lagging and nonlinearity. So it is very difficult to control this process accurately using traditional control theory. In order to guarantee the process to be stable, and to produce the high-grade cement clinker, it is important to make the temperature of the sintering zone stable. Artificial neural networks offer a solution to this problem due to their advantages, such as self-organization, self-adaptivity and fault tolerance. This paper introduces a novel nonlinear optimal neuro-controller which is based on adaptive critic design and uses the structure of action-dependant heuristic dynamic programming (ADHDP). The principle of ADHDP is presented. An action network and a critic network are set up in such a way that they basically learn from interactions based on local measurement to optimize the neuro-controller. The ADHDP neuro-controller has a simple frame-work and is independent from the system model. A simulation of the cement rotary kiln is carried out using Matlab/Simulink. The simulation results show that using the ADHDP neuro-controller it is possible to keep the temperature of sintering zone stable in a certain range, and the temperature can meet the requirements of cement clinker production. Simulation results also are presented to show that the neuro-controller with the ACD has the potential to control the cement rotary kiln.

关键词： cement rotary kiln the sintering zone model adaptive critic designs artificial neural network action-dependant heuristic dynamic programming (ADHDP)

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Using ADP to understand and replicate brain intelligence: the next level design

Using ADP to understand and replicate brain intelligence: th...

引用

ieee International symposium on Approximate dynamic programming and reinforcement learning

作者： Werbos, Paul J. Natl Sci Fdn Arlington VA 22203 USA

ISBN: (纸本)9781424407064

Since the 1960's I proposed that we could understand and replicate the highest level of intelligence seen in the brain, by building ever more capable and general systems for adaptive dynamic programming (ADP) - like "reinforcement learning" but based on approximating the Bellman equation and allowing the controller to know its utility function. Growing empirical evidence on the brain supports this approach. adaptive critic systems now meet tough engineering challenges and provide a kind of first-generation model of the brain. Lewis, Prokhorov and myself have early second-generation work. Mammal brains possess three core capabilities creativity/imagination and ways to manage spatial and temporal complexity - even beyond the second generation. This paper reviews previous progress, and describes new tools and approaches to overcome the spatial complexity gap.

关键词： dynamic programming

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Protecting against evaluation overfitting in empirical reinforcement learning

Protecting against evaluation overfitting in empirical reinf...

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ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Whiteson, Shimon Tanner, Brian Taylor, Matthew E. Stone, Peter Informatics Institute University of Amsterdam Netherlands Department of Computing Science University of Alberta Canada Department of Computer Science Lafayette College United States Department of Computer Science University of Texas Austin United States

ISBN: (纸本)9781424498888

Empirical evaluations play an important role in machine learning. However, the usefulness of any evaluation depends on the empirical methodology employed. Designing good empirical methodologies is difficult in part because agents can overfit test evaluations and thereby obtain misleadingly high scores. We argue that reinforcement learning is particularly vulnerable to environment overfitting and propose as a remedy generalized methodologies, in which evaluations are based on multiple environments sampled from a distribution. In addition, we consider how to summarize performance when scores from different environments may not have commensurate values. Finally, we present proof-of-concept results demonstrating how these methodologies can validate an intuitively useful range-adaptive tile coding method. © 2011 ieee.

关键词： reinforcement learning

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reinforcement learning algorithms for solving classification problems

Reinforcement learning algorithms for solving classification...

引用

ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Wiering, Marco A. Van Hasselt, Hado Pietersma, Auke-Dirk Schomaker, Lambert Dept. of Artificial Intelligence University of Groningen Netherlands Multi-agent and Adaptive Computation Centrum Wiskunde en Informatica Netherlands

ISBN: (纸本)9781424498888

We describe a new framework for applying reinforcement learning (RL) algorithms to solve classification tasks by letting an agent act on the inputs and learn value functions. This paper describes how classification problems can be modeled using classification Markov decision processes and introduces the Max-Min ACLA algorithm, an extension of the novel RL algorithm called actor-critic learning automaton (ACLA). Experiments are performed using 8 datasets from the UCI repository, where our RL method is combined with multi-layer perceptrons that serve as function approximators. The RL method is compared to conventional multi-layer perceptrons and support vector machines and the results show that our method slightly outperforms the multi-layer perceptron and performs equally well as the support vector machine. Finally, many possible extensions are described to our basic method, so that much future research can be done to make the proposed method even better. © 2011 ieee.

关键词： Support vector machines

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Using reward-weighted regression for reinforcement learning of task space control

Using reward-weighted regression for reinforcement learning ...

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ieee International symposium on Approximate dynamic programming and reinforcement learning

作者： Peters, Jan Schaal, Stefan Univ So Calif Los Angeles CA 90089 USA

ISBN: (纸本)9781424407064

Many robot control problems of practical importance, including task or operational space control, can be reformulated as immediate reward reinforcement learning problems. However, few of the known optimization or reinforcement learning algorithms can be used in online learning control for robots, as they are either prohibitively slow, do not scale to interesting domains of complex robots, or require trying out policies generated by random search, which are infeasible for a physical system. Using a generalization of the EM-base reinforcement learning framework suggested by Dayan & Hinton, we reduce the problem of learning with immediate rewards to a reward-weighted regression problem with an adaptive, integrated reward transformation for faster convergence. The resulting algorithm is efficient, learns smoothly without dangerous jumps in solution space, and works well in applications of complex high degree-of-freedom robots.

关键词： reinforcement learning

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dynamic lead time promising

Dynamic lead time promising

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ieee symposium on adaptive dynamic programming and reinforcement learning

作者： Reindorp, Matthew J. Fu, Michael C. Department of Industrial Engineering and Innovation Sciences Eindhoven University of Technology Netherlands Robert H. Smith School of Business Institute for Systems Research University of Maryland United States

ISBN: (纸本)9781424498888

We consider a make-to-order business that serves customers in multiple priority classes. Orders from customers in higher classes bring greater revenue, but they expect shorter lead times than customers in lower classes. In making lead time promises, the firm must recognize preexisting order commitments, uncertainty over future demand from each class, and the possibility of supply chain disruptions. We model this scenario as a Markov decision problem and use reinforcement learning to determine the firm's lead time policy. In order to achieve tractability on large problems, we utilize a sequential decision-making approach that effectively allows us to eliminate one dimension from the state space of the system. Initial numerical results from the sequential dynamic approach suggest that the resulting policies more closely approximate optimal policies than static optimization approaches. © 2011 ieee.

关键词： reinforcement learning

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