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Free energy based policy gradients

Free energy based policy gradients

引用

IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, (ADPRL)

作者： Evangelos A. Theodorou Jiri Najemnik Emo Todorov Department of Computer Science and Engineering University of Washington Seattle University of Washington Seattle Departments of Computer Science and Engineering and Applied Math University of Washington Seattle

Despite the plethora of reinforcement learning algorithms in machine learning and control, the majority of the work in this area relies on discrete time formulations of stochastic dynamics. In this work we present a new policy gradient algorithm for reinforcement learning in continuous state action spaces and continuous time for free energy-like cost functions. The derivation is based on successive application of Girsanov's theorem and the use of the Radon Nikodým derivative as formulated for Markov diffusion processes. The resulting policy gradient is reward weighted. The use of Radon Nikodým extends analysis and results to more general models of stochasticity in which jump diffusions processes are considered. We apply the resulting algorithm in two simple examples for learning attractor landscapes in rhythmic and discrete movements.

关键词： Learning (artificial intelligence) Diffusion processes Markov processes Cost function Equations Heuristic algorithms

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Time Varying Nonlinear Policy Gradients

Time Varying Nonlinear Policy Gradients

引用

IEEE Annual Conference on Decision and Control

作者： Evangelos A. Theodorou Krishnamurthy Dvijotham Emo Todorov School of Aerospace Engineering Georgia Institute of Technology Atlanta Department of Computer Science and Engineering University of Washington Seattle Departments of Computer Science and Engineering and Applied Math University of Washington Seattle

ISBN: (纸本)9781467357159

We derive Policy Gradients(PGs) with time varying parameterizations for nonlinear diffusion processes affine in noise. The resulting policies have the form of reward weighted gradient. The analysis is in continuous time and includes the case of linear and nonlinear parameterizations. Examples on stochastic control problems for diffusions processes are provided.

关键词： Trajectory Linear programming Noise Diffusion processes Aerodynamics Educational institutions Equations

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Administration and academic staff performance management system using content management system (CMS) technologies

Administration and academic staff performance management sys...

引用

International Conference on Electronics, computer and Computation (ICECCO)

作者： Moussa Mahamat Boukar Isa Muslu Department of Computer Science Nigerian Turkish Nile University Abuja Nigeria Department of Applied Math. International Ataturk Alatoo University Bishkek Kyrgyzstan

Content management systems (CMS) provide an optimal solution by organizing information and, mostly, creating and managing an enterprises knowledge. This paper aims to construct an interactive dynamic system base on CMS technologies, on which a range of problems concerning the administration and academic aspects of a university management system can be analyzed and certain policies for overcoming these problems can be tested and improved. This pilot was developed to explore the versatility of content management system (CMS) and to use the technology of CMS as precursor to handle content of a full blown automation of administration and academic performance management system.

关键词： Content management Educational institutions Instruments Information management Automation Companies

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Using Ontology Patterns for Building a Reference Software Testing Ontology

Using Ontology Patterns for Building a Reference Software Te...

引用

International Workshop on Enterprise Distributed Object Computing, EDOCW

作者： E.F. Souza R.A. Falbo N.L. Vijaykumar Applied Computing National Institute for Space Research INPE São José dos Campos SP Brazil Department of Computer Science Federal University of Espírito Santo UFES Vitória ES Brazil Lab. of Comp. and Applied Math. National Institute for Space Research INPE São José dos Campos SP Brazil

Software testing is a critical process for achieving product quality. Its importance is more and more recognized, and there is a growing concern in improving the accomplishment of this process. In this context, Knowledge Management emerges as an important supporting tool. However, managing relevant knowledge to reuse is difficult and it requires some means to represent and to associate semantics to a large volume of test information. In order to address this problem, we have developed a Reference Ontology on Software Testing (ROost). ROost is built reusing ontology patterns from the Software Process Ontology Pattern Language (SP-OPL). In this paper, we discuss how ROost was developed, and present a fragment of Roost that concerns with software testing process, its activities, artifacts, and procedures.

关键词： Ontologies Software testing Unified modeling language Software Standards Context

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Active learning with distributional estimates

Active learning with distributional estimates

引用

28th Conference on Uncertainty in Artificial Intelligence, UAI 2012

作者： Röder, Jens Nadler, Boaz Kunzmann, Kevin Hamprecht, Fred A. CR/AEM Robert Bosch GmbH Hildesheim Germany Department of Computer Science and Applied Math Weizmann Institute of Science Rehovot Israel University of Heidelberg Heidelberg Germany

ISBN: (纸本)9780974903989

Active Learning (AL) is increasingly important in a broad range of applications. Two main AL principles to obtain accurate classification with few labeled data are refinement of the current decision boundary and exploration of poorly sampled regions. In this paper we derive a novel AL scheme that balances these two principles in a natural way. In contrast to many AL strategies, which are based on an estimated class conditional probability p(y|x), a key component of our approach is to view this quantity as a random variable, hence explicitly considering the uncertainty in its estimated value. Our main contribution is a novel mathematical framework for uncertainty-based AL, and a corresponding AL scheme, where the uncertainty in p(y|x) is modeled by a second-order distribution. On the practical side, we show how to approximate such secondorder distributions for kernel density classification. Finally, we find that over a large number of UCI, USPS and Caltech- 4 datasets, our AL scheme achieves significantly better learning curves than popular AL methods such as uncertainty sampling and error reduction sampling, when all use the same kernel density classifier.

关键词： Uncertainty analysis

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Approximating equilibria in sequential auctions with incomplete information and multi-unit demand

Approximating equilibria in sequential auctions with incompl...

引用

26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012

作者： Greenwald, Amy Li, Jiacui Sodomka, Eric Department of Computer Science Brown University Providence RI 02912 United States Department of Applied Math/Economics Brown University Providence RI 02912 United States

ISBN: (纸本)9781627480031

In many large economic markets, goods are sold through sequential auctions. Examples include eBay, online ad auctions, wireless spectrum auctions, and the Dutch flower auctions. In this paper, we combine methods from game theory and decision theory to search for approximate equilibria in sequential auction domains, in which bidders do not know their opponents' values for goods, bidders only partially observe the actions of their opponents', and bidders demand multiple goods. We restrict attention to two-phased strategies: first predict (i.e., learn);second, optimize. We use best-reply dynamics [4] for prediction (i.e., to predict other bidders' strategies), and then assuming fixed other-bidder strategies, we estimate and solve the ensuing Markov decision processes (MDP) [18] for optimization. We exploit auction properties to represent the MDP in a more compact state space, and we use Monte Carlo simulation to make estimating the MDP tractable. We show how equilibria found using our search procedure compare to known equilibria for simpler auction domains, and we approximate an equilibrium for a more complex auction domain where analytical solutions are unknown.

关键词： Markov processes

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Runtime verification with state estimation

Runtime verification with state estimation

引用

2nd International Conference on Runtime Verification, RV 2011

作者： Stoller, Scott D. Bartocci, Ezio Seyster, Justin Grosu, Radu Havelund, Klaus Smolka, Scott A. Zadok, Erez Department of Computer Science Stony Brook University United States Department of Applied Math and Statistics Stony Brook University United States Jet Propulsion Laboratory California Institute of Technology United States

ISBN: (纸本)9783642298592

We introduce the concept of Runtime Verification with State Estimation and show how this concept can be applied to estimate the probability that a temporal property is satisfied by a run of a program when monitoring overhead is reduced by sampling. In such situations, there may be gaps in the observed program executions, thus making accurate estimation challenging. To deal with the effects of sampling on runtime verification, we view event sequences as observation sequences of a Hidden Markov Model (HMM), use an HMM model of the monitored program to "fill in" sampling-induced gaps in observation sequences, and extend the classic forward algorithm for HMM state estimation (which determines the probability of a state sequence, given an observation sequence) to compute the probability that the property is satisfied by an execution of the program. To validate our approach, we present a case study based on the mission software for a Mars rover. The results of our case study demonstrate high prediction accuracy for the probabilities computed by our algorithm. They also show that our technique is much more accurate than simply evaluating the temporal property on the given observation sequences, ignoring the gaps. © 2012 Springer-Verlag.

关键词： State estimation

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Stochastic Optimal Control for Nonlinear Markov Jump Diffusion Processes

Stochastic Optimal Control for Nonlinear Markov Jump Diffusi...

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American Control Conference

作者： Evangelos A. Theodorou Emmanuel Todorov Department of Computer Science and Engineering University of Washington Seattle USA Department of Computer Science and Engineering and Department of Applied Math University of Washington Seattle USA

ISBN: (纸本)9781457710957

We consider the problem finite horizon stochastic optimal control for nonlinear markov jump diffusion processes. In particular, by using stochastic calculus for markov jump diffusions processes and the logarithmic transformation of the value function we demonstrate the transformation of the corresponding Hamilton-Jacobi-Bellman (HJB) Partial Differential Equation (PDE) to the backward Chapman Kolmogorov PDE for jump diffusions. Furthermore we derive the Feynman-Kac lemma for nonlinear markov jump diffusions processes and apply it to the transformed HJB equation. Application of the Feynman-Kac lemma yields the solution of the transformed HJB equation. The path integral interpretation is derived. Finally, conclusions and future directions are discussed.

关键词： jump Markov stochastic optimal control logarithmic transformation diffusion Diffusion processes Lemma

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Characterizing the stiffness of a multi-segment flexible arm during motion

Characterizing the stiffness of a multi-segment flexible arm...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： David Held Yoram Yekutieli Tamar Flash Computer Science Department University of Stanford Stanford CA USA Weizmann Institute of Science Rehovot Israel Computer Science Department Hadassah College Jerusalem Israel Department of Computer Science and Applied Math Weizmann Institute of Science Rehovot Israel

A number of robotic studies have recently turned to biological inspiration in designing control schemes for flexible robots. Examples of such robots include continuous manipulators inspired by the octopus arm. However, the control strategies used by an octopus in moving its arms are still not fully understood. Starting from a dynamic model of an octopus arm and a given set of muscle activations, we develop a simulation technique to characterize the stiffness throughout a motion and at multiple points along the arm. By applying this technique to reaching and bending motions, we gain a number of insights that can help a control engineer design a biologically inspired impedance control scheme for a flexible robot arm. The framework developed is a general one that can be applied to any motion for any dynamic model. We also propose a theoretical analysis to efficiently estimate the stiffness analytically given a set of muscle activations. This analysis can be used to quickly evaluate the stiffness for new static configurations and dynamic movements.

关键词： Muscles Force Computational modeling Trajectory Shape Motion segmentation

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Approximating Equilibria in Sequential Auctions with Incomplete Information and Multi-Unit Demand 12

Approximating Equilibria in Sequential Auctions with Incompl...

引用

Annual Conference on Neural Information Processing Systems

作者： Amy Greenwald Jiacui Li Eric Sodomka Department of Computer Science Brown University Providence RI 02912 Department of Applied Math/Economics Brown University Providence RI 02912

ISBN: (纸本)9781627480031

In many large economic markets, goods are sold through sequential auctions. Examples include eBay, online ad auctions, wireless spectrum auctions, and the Dutch flower auctions. In this paper, we combine methods from game theory and decision theory to search for approximate equilibria in sequential auction domains, in which bidders do not know their opponents' values for goods, bidders only partially observe the actions of their opponents', and bidders demand multiple goods. We restrict attention to two-phased strategies: first predict (i.e., learn); second, optimize. We use best-reply dynamics [4] for prediction (i.e., to predict other bidders' strategies), and then assuming fixed other-bidder strategies, we estimate and solve the ensuing Markov decision processes (MDP) [18] for optimization. We exploit auction properties to represent the MDP in a more compact state space, and we use Monte Carlo simulation to make estimating the MDP tractable. We show how equilibria found using our search procedure compare to known equilibria for simpler auction domains, and we approximate an equilibrium for a more complex auction domain where analytical solutions are unknown.

关键词： Markov chain cargo multicell Sequential Decision Theory Game theory state space Monte Carlo technique

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