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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201-210 订阅

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Identifying trajectory classes in dynamic tasks

Identifying trajectory classes in dynamic tasks

引用

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

作者： Stuart O. Anderson Siddhartha S. Srinivasa Robotics Institute Carnegie Mellon University Pittsburgh PA USA Intel Research Pittsburgh Intel Corporation Pittsburgh PA USA

Using domain knowledge to decompose difficult control problems is a widely used technique in robotics. Previous work has automated the process of identifying some qualitative behaviors of a system, finding a decomposition of the system based on that behavior, and constructing a control policy based on that decomposition. We introduce a novel method for automatically finding decompositions of a task based on observing the behavior of a preexisting controller. Unlike previous work, these decompositions define reparameterizations of the state space that can permit simplified control of the system

关键词： State-space methods Automatic control Control systems Motion control dynamic programming Robotics and automation Convergence learning Humans Robot control

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An Optimal ADP Algorithm for a High-Dimensional Stochastic Control Problem

An Optimal ADP Algorithm for a High-Dimensional Stochastic C...

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

作者： Juliana Nascimento Warren Powell Department of Operations Research and Financial Engineering Princeton University Engineering Princeton NJ USA

We propose a provably optimal approximate dynamic programming algorithm for a class of multistage stochastic problems, taking into account that the probability distribution of the underlying stochastic process is not known and the state space is too large to be explored entirely. The algorithm and its proof of convergence rely on the fact that the optimal value functions of the problems within the problem class are concave and piecewise linear. The algorithm is a combination of Monte Carlo simulation, pure exploitation, stochastic approximation and a projection operation. Several applications, in areas like energy, control, inventory and finance, fall under the framework

关键词： Stochastic processes Optimal control Piecewise linear approximation dynamic programming Heuristic algorithms Probability distribution State-space methods Convergence Piecewise linear techniques Approximation algorithms

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The QV family compared to other reinforcement learning algorithms

The QV family compared to other reinforcement learning algor...

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

作者： Marco A. Wiering Hado van Hasselt Department of Artificial Intelligence University of Groningam Netherlands Intelligent Systems Group of Utrecht University Netherlands

This paper describes several new online model-free reinforcement learning (RL) algorithms. We designed three new reinforcement algorithms, namely: QV2, QVMAX, and QVMAX2, that are all based on the QV-learning algorithm, but in contrary to QV-learning, QVMAX and QVMAX2 are off-policy RL algorithms and QV2 is a new on-policy RL algorithm. We experimentally compare these algorithms to a large number of different RL algorithms, namely: Q-learning, Sarsa, R-learning, Actor-Critic, QV-learning, and ACLA. We show experiments on five maze problems of varying complexity. Furthermore, we show experimental results on the cart pole balancing problem. The results show that for different problems, there can be large performance differences between the different algorithms, and that there is not a single RL algorithm that always performs best, although on average QV-learning scores highest.

关键词： learning Neural networks Probability distribution Stochastic systems Optimal control Algorithm design and analysis Differential equations

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Online reinforcement learning Neural Network Controller Design for Nanomanipulation

Online Reinforcement Learning Neural Network Controller Desi...

引用

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

作者： Qinmin Yang S. Jagannathan Department of Electrical & Computer Engineering University of Missouri Rolla MO USA

In this paper, a novel reinforcement learning neural network (NN)-based controller, referred to adaptive critic controller, is proposed for affine nonlinear discrete-time systems with applications to nanomanipulation. In the online NN reinforcement learning method, one NN is designated as the critic NN, which approximates the long-term cost function by assuming that the states of the nonlinear systems is available for measurement. An action NN is employed to derive an optimal control signal to track a desired system trajectory while minimizing the cost function. Online updating weight tuning schemes for these two NNs are also derived. By using the Lyapunov approach, the uniformly ultimate boundedness (UUB) of the tracking error and weight estimates is shown. Nanomanipulation implies manipulating objects with nanometer size. It takes several hours to perform a simple task in the nanoscale world. To accomplish the task automatically the proposed online learning control design is evaluated for the task of nanomanipulation and verified in the simulation environment

关键词： learning Neural networks Nonlinear control systems Control systems Cost function Programmable control adaptive control Nonlinear systems Optimal control Trajectory

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Q-learning with Continuous State Spaces and Finite Decision Set

Q-Learning with Continuous State Spaces and Finite Decision ...

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

作者： Kengy Barty Pierre Girardeau Jean-Sebastien Roy Cyrille Strugarek EDF Research and Development Clamart France

This paper aims to present an original technique in order to compute the optimal policy of a Markov decision problem with continuous state space and discrete decision variables. We propose an extension of the Q-learning algorithm introduced in 1989 by Watkins for discrete Markov decision problems. Our algorithm relies on stochastic approximation and functional estimation, and uses kernels to locally update the Q-functions. We state under mild assumptions a converge theorem for this algorithm. Finally, we illustrate our algorithm by solving two classical problems: the mountain car task and the puddle world task

关键词： State-space methods Kernel Costs dynamic programming Stochastic processes Recursive estimation Random variables learning Approximation algorithms Uncertainty

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A theoretical and empirical analysis of Expected Sarsa

A theoretical and empirical analysis of Expected Sarsa

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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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Kalman Temporal Differences: The deterministic case

Kalman Temporal Differences: The deterministic case

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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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A Theoretical Analysis of Cooperative Behavior in Multi-agent Q-learning

A Theoretical Analysis of Cooperative Behavior in Multi-agen...

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

作者： Ludo Waltman Uzay Kaymak Erasmus Erasmus University Rotterdam Rotterdam Netherlands

A number of experimental studies have investigated whether cooperative behavior may emerge in multi-agent Q-learning. In some studies cooperative behavior did emerge, in others it did not. This paper provides a theoretical analysis of this issue. The analysis focuses on multi-agent Q-learning in iterated prisoner's dilemmas. It is shown that under certain assumptions cooperative behavior may emerge when multi-agent Q-learning is applied in an iterated prisoner's dilemma. An important consequence of the analysis is that multi-agent Q-learning may result in non-Nash behavior. It is found experimentally that the theoretical results presented in this paper are quite robust to violations of the underlying assumptions

关键词： Helium Oligopoly Nash equilibrium dynamic programming learning Environmental economics Robustness Performance analysis Algorithm design and analysis Microeconomics

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Evaluation of Policy Gradient Methods and Variants on the Cart-Pole Benchmark

Evaluation of Policy Gradient Methods and Variants on the Ca...

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

作者： Martin Riedmiller Jan Peters Stefan Schaal NeuroInformatics Group University of Osnabrück Germany Computational Learning and Motor Control University of Southern California USA

In this paper, we evaluate different versions from the three main kinds of model-free policy gradient methods, i.e., finite difference gradients, 'vanilla' policy gradients and natural policy gradients. Each of these methods is first presented in its simple form and subsequently refined and optimized. By carrying out numerous experiments on the cart pole regulator benchmark we aim to provide a useful baseline for future research on parameterized policy search algorithms. Portable C++ code is provided for both plant and algorithms; thus, the results in this paper can be reevaluated, reused and new algorithms can be inserted with ease

关键词： Gradient methods learning Finite difference methods Solids Legged locomotion Stochastic processes dynamic programming Motor drives Optimization methods Regulators

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Efficient learning in Cellular Simultaneous Recurrent Neural Networks - The Case of Maze Navigation Problem

Efficient Learning in Cellular Simultaneous Recurrent Neural...

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

作者： Roman Ilin Robert Kozma Paul J. Werbos Department of Mathematical Sciences University of Memphis Memphis TN USA National Science Foundation Arlington VA USA

Cellular simultaneous recurrent neural networks (SRN) show great promise in solving complex function approximation problems. In particular, approximate dynamic programming is an important application area where SRNs have significant potential advantages compared to other approximation methods. learning in SRNs, however, proved to be a notoriously difficult problem, which prevented their broader use. This paper introduces an extended Kalman filter approach to train SRNs. Using the two-dimensional maze navigation problem as a testbed, we illustrate the operation of the method and demonstrate its benefits in generalization and testing performance

关键词： Cellular networks Recurrent neural networks Motion planning Function approximation dynamic programming Electronic mail Testing Cost function Equations Feedforward systems

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