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检索条件"主题词=Q-Learning algorithm"

共 197 条记录，以下是141-150 订阅

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Synchronization of Probabilistic Boolean Networks under State-Flipped Control 39

Synchronization of Probabilistic Boolean Networks under Stat...

引用

39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Bian, Chenyang Du, Leihao Zhang, Zhipeng Tiangong Univ Sch Control Sci & Engn Tianjin Peoples R China Tiangong Univ Sch Comp Sci & Engn Tianjin Peoples R China Tiangong Univ Sch Artificial Intelligence Tianjin Peoples R China

ISBN: (纸本)9798350390780;9798350379228

This paper investigates the synchronization problem of probabilistic boolean networks (PBNs) under state-flipped control. First, by flipping some of the nodes, the entire state space is transferred to a synchronous state set. Some verification conditions for the synchronization of PBNs are proposed. Second, a q-learning (qL) algorithm for synchronizing PBNs in finite flip control is given, and the minimum flip set is obtained. Finally, numerical simulations are performed to verify the feasibility of the conclusions.

关键词： Probabilistic boolean networks State flip control q-learning algorithm Synchronization

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Comparative Study of Reinforcement learning algorithms to solve Reconfigurable Facilities Layout Problem 18

Comparative Study of Reinforcement Learning Algorithms to so...

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18th IFAC Symposium on Information Control Problems in Manufacturing (INCOM)

作者： Chiboub, Amine Francois, Julien Alix, Thecle Dupas, Remy Univ Bordeaux IMS Bordeaux INP IMSCNRSUMR 5218 F-33400 Talence France Univ Bordeaux IMS Bordeaux INP I2MCNRSUMR 5295 F-33400 Talence France

Facilities Layout Problems (FLPs) aim to efficiently allocate facilities within a given space, considering various constraints such as minimizing transportation distances. These problems are commonly encountered in various types of advanced manufacturing systems, including Reconfigurable Manufacturing Systems (RMSs). RMSs enable easier layout changes to accommodate shifts in product mix, production volume, or process requirements thanks to their modularity and changeability. Reinforcement learning (RL) has proven its efficiency in addressing decision-making problems. Therefore, this paper introduces a comparative study between two RL algorithms to solve FLPs: Advantage Actor-Critic (A2C) and q-learning algorithms. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Facilities Layout Problem Reconfigurable Manufacturing Systems Reinforcement learning q-learning algorithm A2C algorithm

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Practical aspects of the model-free learning control initialization 20

Practical aspects of the model-free learning control initial...

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20th International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Stebel, Krzysztof Silesian Tech Univ Inst Automat Control Gliwice Poland

ISBN: (纸本)9781479987016

The paper presents aspects of model-free learning control initialization. Model-free learning has several advantages as general purpose approach or adaptive capability. However, practical implementation is not intuitive in each step. Choice of time scale in order to provide the necessary reactivity or level of granularity of states or control actions is not an easy problem. Before it learns environment it may disturb process control in an unacceptable way. This is the motivation to find rules for easer initialization and controller set-up.

关键词： model-free control intelligent control learning control process control online learning q-learning algorithm

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Dynamic Enhanced Inter-Cell Interference Coordination using Reinforcement learning Approach in Heterogeneous Network

Dynamic Enhanced Inter-Cell Interference Coordination using ...

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15th IEEE International Conference on Communication Technology (ICCT)

作者： Li, qi Xia, Hailun Zeng, Zhimin Zhang, Tiankui Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China

ISBN: (纸本)9781479900749

This paper investigates enhanced Inter-Cell Interference Coordination (eICIC) techniques for Heterogeneous Networks (HetNets), and models this strategic coexistence as a multi-player system in which interference management strategies inspired from a form of reinforcement learning known as distributed q-learning are devised. Specifically, this paper focuses on time domain eICIC techniques in which each macrocell optimizes its Almost Blank Subframe (ABS) configuration consisted of ABS density and reduces power level. Instead of relying on predefined configuration, the system is designed to learn optimal ABS configurations by directly interacting with the environment. To substantiate our theoretical findings, system level simulations are carried out in which our proposed solution is compared with the conventional approach that ABS configuration is fixed. Our proposed solution is shown to yield substantial gains of user throughput compared to fixed ABS configuration.

关键词： Heterogeneous Networks (HetNet) enhanced Inter-Cell Interference Coordination (eICIC) q-learning algorithm Almost Blank Subframe (ABS) configuration

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Multi-agent reinforcement learning for strategic bidding in power markets

Multi-agent reinforcement learning for strategic bidding in ...

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3rd IEEE International Conference on Intelligent Systems

作者： Tellidou, Athina C. Bakirtzis, Anastasios G. Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki 54124 Greece

ISBN: (纸本)9781424401956

In the agent-based simulation discussed in this paper, we study the dynamics of the power market, when suppliers act following a q-learning based bidding strategy. Power suppliers aim to satisfy two objectives: the maximization of their profit and their utilization rate. To meet with success their goals, they need to acquire a complex behavior by learning through a continuous exploiting and exploring process. Reinforcement learning theory provides a formal framework, along with a family of learning methods. In this paper we use q-learning algorithm, perhaps the most popular among temporal difference methods. q-learning offers suppliers the ability to evaluate their actions and to retain the most profitable of them. A five bus power system is used for our case studies;our experiments are contacted with three supplier-agents in all cases but the last one where nine agents participate. The Locational Marginal Pricing (LMP) system serves as the market clearing mechanism.

关键词： electricity spot markets multi-agent modeling q-learning algorithm reinforcement learning supplier bidding strategy

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Design of Reinforce learning Control algorithm and Verified in Inverted Pendulum 34

Design of Reinforce Learning Control Algorithm and Verified ...

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第三十四届中国控制会议

作者： WANG Linglin LIU Yongxin ZHAI Xiaoke College of electronic information engineering Inner Mongolia University

ISBN: (纸本)9781467374439

The reinforce leaning control algorithm is studied in this *** algorithms are designed using one-stage inverted pendulum as an *** is q-learning control algorithm,the other is also this algorithm but its parameters are optimized by BP neural *** are verified in one-stage inverted pendulum swing up *** results show that the algorithm with BP NN optimized is more effective and stable,and this algorithm can be used in real time control.

关键词： learning control q-learning algorithm BP network Inverted pendulum

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Designing Model-free Control Law for Slowly Varying Nonlinear Systems Based on Reinforcement learning 25

Designing Model-free Control Law for Slowly Varying Nonlinea...

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25th Iranian Conference on Electrical Engineering (ICEE)

作者： Noori, Amin Sadrnia, Mohammad Ali Sistani, Mohammad Bagher Naghibi Shahrood Univ Technol Control Engn Shahrood Iran Ferdowsi Univ Mashhad Control Engn Mashhad Iran

ISBN: (纸本)9781509059638

A method to control general slowly varying nonlinear systems based on reinforcement learning is proposed. Based on the q-Ieaning algorithm a model-free control signal is designed. However, this control signal is numerical and also not smooth enough. Thus a polynomial is fitted to the control signal obtained by q-Iearning algorithm. A larger degree of polynomial leads to smaller fitting error. By this procedure, we have a smooth control signal which is easy to implement, moreover, the control signal is in a closed form and is not numerical any longer. This permits deep, elegant and powerful mathematical analysis of the nonlinear system and many properties of the system such as stability, controllability, chaos, limit cycles can be studied. The efficiency of the proposed technique is proved by using it in an example.

关键词： Reinforcement learning Slowly varying nonlinear systems q-learning algorithm

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Cost-Efficient Computation Offloading in VEC Using Deep Reinforcement learning Techniques 20

Cost-Efficient Computation Offloading in VEC Using Deep Rein...

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20th IEEE International Wireless Communications and Mobile Computing (IEEE IWCMC) - Green and Intelligent Communications

作者： Wang, Bingxin Tu, Dan Wang, Jie China Telecom Corp Ltd Beijing Res Inst Beijing Peoples R China

ISBN: (纸本)9798350361261;9798350361278

The rapid progression in autonomous driving and vehicle networking technologies has catalyzed the emergence of advanced vehicle applications, aiming to augment traffic safety and the driving experience. This advancement, however, is challenged by the limited computational and storage capacities inherent in on-board vehicle systems. To address this, Vehicle Edge Computing (VEC) emerges as a pivotal solution, enhancing vehicular computational capabilities. In this context, we introduce a novel VEC task offloading model utilizing Deep Reinforcement learning (DRL). This model leverages the otherwise idle computational resources available in vehicles to facilitate efficient edge computing offloading within heterogeneous networks. A key innovation in our approach is the integration of Reinforcement learning (RL) with Deep learning (DL), significantly improving the convergence efficiency of the system. We also introduce an enhanced q-learning algorithm tailored to jointly address the task offloading and processing challenges in VEC. This algorithm is adept at making optimal offloading decisions, aiming to minimize the overall system cost, encompassing both latency and energy consumption. Through rigorous simulation, our results demonstrate that this improved q-learning approach substantially reduces total system costs while concurrently improving the quality of service in VEC environments. Our study not only offers a robust framework for computation offloading in vehicular networks but also paves the way for future research in AI-driven vehicular technology optimizations.

关键词： Vehicle Edge Computing Autonomous Driving Heterogeneous Networks Computation Offloading q-learning algorithm System Cost Minimization quality of Service Vehicular Networking AI in Transportation Edge Computing Efficiency

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Study on motion forms of a two-dimensional mobile robot by using reinforcement learning

Study on motion forms of a two-dimensional mobile robot by u...

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SICE-ICASE International Joint Conference

作者： Jung, Youngmi Inoue, Masashi Hara, Masayuki Huang, Jian Yabuta, Tetsuro Yokohama Natl Univ Dept Mech Engn Grad Sch Engn Yokohama Kanagawa 240 Japan

ISBN: (纸本)9788995003848

The main advantage of Reinforcement learning is that it provides unexpected solutions for a designer. This study shows how a mobile robot can obtain unexpected motion forms by using Reinforcement learning. Results show the the mobile robot with two-dimensional mobile ability can obtain unexpected motion forms for both advance motion and rotation motion. The mechanisms for these motions were investigated in order to understand how to obtain these motions. Moreover, since this system has a two-dimensional factor, this study examines the learning characteristic for the oblivion of the learning knowledge. In addition, this study examines the learning of the knowledge manipulation method to obtain new learning results with respect to the two-dimensional factor.

关键词： Reinforcement learning method q-learning algorithm mobile robot new learning motion forms

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A GREEN INTELLIGENT UNICAST ROUTING algorithm

A GREEN INTELLIGENT UNICAST ROUTING ALGORITHM

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5th IEEE International Conference on Broadband Network & Multimedia Technology (IC-BNMT)

作者： Zhang, Jinhong Wang, Xingwei Huang, Min Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781479900930

For the past years, the energy overconsumption problems, arising from the rapid growth of Internet scale and services types, are becoming more and more serious. In this context, the Information and Communication Technology (ICT) sector has given an extensive concern to the research on green networking. From an energy-saving point of view for Internet, this paper designs a power consumption model and a qoS model for unicast. Furthermore, this paper proposes a unicast routing algorithm based on Chandy-Misra algorithm and q-learning algorithm for green Internet, which is compared with an ant colony algorithm based self-adaptive energy saving routing with respect to power consumption, the success rate of routing and running time. Results show the intelligent unicast routing algorithm proposed can effectively reduce network energy consumption, while guaranteeing the good performance.

关键词： Green Internet Unicast routing Energy saving Chandy-Misra algorithm q-learning algorithm

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