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检索条件"任意字段=IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning"

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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, (ADPRL)

作者： Lucian Buşoniu Damien Ernst Bart De Schutter Robert Babuška Delft Center of Systems & Control Delft University of Technnology Netherlands FRS-FNRS Systems and Modeling Unit University of Liège Belgium

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.

关键词： Approximation algorithms Equations Function approximation Trajectory Markov processes Mathematical model

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Adaptive sample collection using active learning for kernel-based approximate policy iteration

Adaptive sample collection using active learning for kernel-...

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ieee symposium on Adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Chunming Liu Xin Xu Haiyun Hu Bin Dai College of Mechatronics and Automation National University of Defense Technology Changsha China

approximate policy iteration (API) has been shown to be a class of reinforcement learning methods with stability and sample efficiency. However, sample collection is still an open problem which is critical to the performance of API methods. In this paper, a novel adaptive sample collection strategy using active learning-based exploration is proposed to enhance the performance of kernel-based API. In this strategy, an online kernel-based least squares policy iteration (KLSPI) method is adopted to construct nonlinear features and approximate the Q-function simultaneously. Therefore, more representative samples can be obtained for value function approximation. Simulation results on typical learning control problems illustrate that by using the proposed strategy, the performance of KLSPI can be improved remarkably.

关键词： Kernel Algorithm design and analysis Function approximation Approximation algorithms learning Dictionaries

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A novel approach for constructing basis functions in approximate dynamic programming for feedback control

A novel approach for constructing basis functions in approxi...

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ieee symposium on Adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Jian Wang Zhenhua Huang Xin Xu College of Mechatronics and Automation National University of Defense Tech Changsha P. R. China

This paper presents a novel approach for constructing basis functions in approximate dynamic programming (ADP) through the locally linear embedding (LLE) process. It considers the experience (sample) data as a high-dimensional space and the basis functions to be solved as a low-dimensional space. Through mapping the high-dimensional data into a single global coordinate system of lower dimensionality, the solved basis functions in low-dimensional space have the property that nearby experience data in the high dimensional space remain nearby and similarly co-located with respect to one in the low dimensional space. Thus, the obtained basis functions can precisely approximate the real value/action-value function. The simulation results show that the basis functions obtained by LLE can represent the final policy with a higher precision.

关键词： learning (artificial intelligence) Function approximation dynamic programming Equations Linear approximation Vectors

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An adaptive dynamic programming algorithm to solve optimal control of uncertain nonlinear systems

An adaptive dynamic programming algorithm to solve optimal c...

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ieee symposium on Adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Xiaohong Cui Yanhong Luo Huaguang Zhang School of Information Science and Engineering Northeastern University Shenyang Liaoning China

ISBN: (纸本)9781479945511

In this paper, an approximate optimal control method based on adaptive dynamic programming(ADP) is discussed for completely unknown nonlinear system. An online critic-action-identifier algorithm is developed using neural network systems, where the criticaction networks approximate the optimal value function and optimal control and the other two neural networks approximates the unknown system. Furthermore the adaptive tuning laws are given based on Lyapunov approach, which ensures the uniform ultimate bounded stability of the closed-loop system. Finally, the effectiveness is demonstrated by a simulation example.

关键词： Optimal control Artificial neural networks Mathematical model Equations Heuristic algorithms Function approximation

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Opposition-Based Q(λ) with Non-Markovian Update

Opposition-Based Q(λ) with Non-Markovian Update

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ieee symposium on Adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Maryam Shokri Hamid R. Tizhoosh Mohamed S. Kamel Pattern Analysis and Machine Intelligence Laboratory Department of Systems Design Engineering University of Waterloo ONT Canada Department of Electrical and Computer Engineering University of Waterloo ONT Canada

The OQ(λ) algorithm benefits from an extension of eligibility traces introduced as opposition trace. This new technique is a combination of the idea of opposition and eligibility traces to deal with large state space problems in reinforcement learning applications. In our previous works the comparison of the results of OQ(λ) and conventional Watkins' Q(λ) reflected a remarkable increase in performance for the OQ(λ) algorithm. However the Markovian update of opposition traces is an issue which is investigated in this paper. It has been assumed that the opposite state can be presented to the agent. This may limit the usability of the technique to deterministic environments. In order to relax this assumption the non-Markovian opposition-based Q(λ) (NOQ(λ)) is introduced in this work. The new method is a hybrid of Markovian update for eligibility traces and non-Markovian-based update for opposition traces. The experimental results show improvements of learning speed for the proposed technique compared to Q(λ) and OQ(λ). The new technique performs faster than OQ(λ) algorithm with the same success rate and can be employed for broader range of applications since it does not require determining state transition

关键词： learning Usability dynamic programming Pattern analysis Machine intelligence Laboratories System analysis and design Design engineering Systems engineering and theory State-space methods

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Automatically customizing a powered knee prosthesis with human in the loop using adaptive dynamic programming

Automatically customizing a powered knee prosthesis with hum...

引用

2017 international symposium on Wearable Robotics and Rehabilitation, WeRob 2017

作者： Wen, Yue Brandt, Andrea Si, Jennie Huang, He Helen NCSU UNC Department of Biomedical Engineering NC State University RaleighNC27695-7115 United States University of North Carolina Chapel HillNC27599 United States Department of Electrical Computer and Energy Engineering Arizona State University TempeAZ85281 United States

ISBN: (纸本)9781538643778

In this study, we validated a human-in-the-loop auto-tuner using machine learning to automatically customize powered knee prosthesis control parameters for an amputee subject in real time. The experimental powered knee prosthesis was controlled by a finite state impedance controller, which had 12 configurable impedance control parameters. Using adaptive dynamic programming with reinforcement learning while one transfemoral amputee subject walked with the powered knee prosthesis, the auto-tuner would interact with the human-prosthesis system and learn to configure the high dimension of control parameters. We tested 4 different initial conditions with one unilateral transfemoral amputee subject. The results showed that the auto-tuner discovered the control parameters that allowed amputee subject to generate normative knee kinematics in 3 out of 4 tuning sessions. For all test sessions, the averaged root-mean-square error of the knee kinematics relative to the normative knee kinematics decreased from 6.6 degrees to 4.6 degrees after tuning procedure. © 2017 ieee.

关键词： Adaptive control systems

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Hybrid Ant Colony Optimization Using Memetic Algorithm for Traveling Salesman Problem

Hybrid Ant Colony Optimization Using Memetic Algorithm for T...

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ieee symposium on Adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Haibin Duan Xiufen Yu School of Automation Science and Electrical Engineering Beihang University Beijing China Center for Space Science and Applied Research Chinese Academy and Sciences Beijing China

Ant colony optimization was originally presented under the inspiration during collective behavior study results on real ant system, and it has strong robustness and easy to combine with other methods in optimization. Although ant colony optimization for the heuristic solution of hard combinational optimization problems enjoy a rapidly growing popularity, but little research is conducted on the optimum configuration strategy for the adjustable parameters in the ant colony optimization, and the performance of ant colony optimization depends on the appropriate setting of parameters which requires both human experience and luck to some extend. Memetic algorithm is a population-based heuristic search approach which can be used to solve combinatorial optimization problem based on cultural evolution. Based on the introduction of these two meta-heuristic algorithms, a novel kind of adjustable parameters configuration strategy based on memetic algorithm is developed in this paper, and the feasibility and effectiveness of this approach are also verified through the famous traveling salesman problem (TSP). This hybrid approach is also valid for other types of combinational optimization problems

关键词： Ant colony optimization Traveling salesman problems Cultural differences Feedback Genetic algorithms Biological cells dynamic programming learning Automation Robustness

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A Combined Policy Gradient and Q-learning Method for Data-driven Optimal Control Problems 9

A Combined Policy Gradient and Q-learning Method for Data-dr...

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9th international Conference on Information Science and Technology (ICIST)

作者： Lin, Mingduo Liu, Derong Zhao, Bo Dai, Qionghai Dong, Yi Guangdong Univ Technol Sch Automat Guangzhou Peoples R China Beijing Normal Univ Sch Syst Sci Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Beijing Inst Technol Sch Opt & Photon Beijing Peoples R China

ISBN: (纸本)9781728121062

This paper focuses on the data-driven controller design for optimal control problems of nonlinear nonaffine discrete-time systems. A novel policy gradient and Q-learning (PGQL) adaptive algorithm which learns the optimal control policy from real empirical data is developed without requiring system dynamics. A policy iteration scheme is designed to iteratively update the approximate Q-function, and the control policy is improved via gradient method until they converge to the bounded regions of the optimal Q-function and the optimal control policy, respectively. Two neural networks (NNs) are employed to realize the developed algorithm. Moreover, the convergence analysis of approximate Q-function is established. Since the control policy is parameterized, it can be upgraded through updating the actor-NN parameters in the direction of the performance gradient. Finally, the simulation results are given to verify the performance of the developed PGQL adaptive algorithm.

关键词： adaptive dynamic programming optimal control reinforcement learning policy gradient Q-learning data-driven

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Resource Provisioning in Fog Computing through Deep reinforcement learning 17

Resource Provisioning in Fog Computing through Deep Reinforc...

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17th IFIP/ieee international symposium on Integrated Network Management, IM 2021

作者： Santos, Jose Wauters, Tim Volckaert, Bruno Turck, Filip De Ghent University - Imec IDLab Department of Information Technology Technologiepark-Zwijnaarde 126 Gent9052 Belgium

ISBN: (纸本)9783903176324

The massive growth of connected devices has made traditional cloud systems inadequate to sustain the scalability, mobility, and heterogeneous nature of the Internet of Things (oT). Distributed clouds have become a potential business opportunity for many service providers enabling the deployment of services on computational resources from the cloud up to the edge. However, challenges persist in fog-cloud infrastructures. One of them is known as Service Function Chaining (SFC), where providers benefit from network softwarization to create virtual chains of connected micro-services. Research has tackled SFC Allocation (SFCA) through theoretical modeling and heuristic algorithms, which often cannot cope with the dynamic behavior of the network. Recent works have addressed these challenges through Machine learning (ML), which can be capable of dynamically reconfiguring cloud-native service requirements over the continuum of virtual resources in next-generation networks. Thus, in this paper, a Deep reinforcement learning (DRL) approach is proposed for SFCA in Fog Computing focused on energy efficiency. Our agent learns about the best resource allocation decisions, focused on reducing costs from a previously presented Mixed-integer linear programming (MILP) formulation. Results show that our agent achieves comparable performance to state-of-the-art MILP formulations during dynamic use cases, obtaining 95% of request acceptance. © 2021 IFIP.

关键词： Fog computing

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A unified framework for temporal difference methods

A unified framework for temporal difference methods

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ieee symposium on Adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Dimitri P. Bertsekas Laboratory of Information and Decision Systems (LIDS) Massachusetts Institute of Technology MA USA

We propose a unified framework for a broad class of methods to solve projected equations that approximate the solution of a high-dimensional fixed point problem within a subspace S spanned by a small number of basis functions or features. These methods originated in approximate dynamic programming (DP), where they are collectively known as temporal difference (TD) methods. Our framework is based on a connection with projection methods for monotone variational inequalities, which involve alternative representations of the subspace S (feature scaling). Our methods admit simulation-based implementations, and even when specialized to DP problems, include extensions/new versions of the standard TD algorithms, which offer some special implementation advantages and reduced overhead.

关键词： Least squares approximation Difference equations Jacobian matrices Laboratories dynamic programming Probability distribution Costs Books Least squares methods Linear matrix inequalities

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