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检索条件"主题词=Q-learning algorithm"

共 197 条记录，以下是151-160 订阅

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Dynamic Joint Decision on Price and Delivery Date in MTO Manufacturer Based on Agent

Dynamic Joint Decision on Price and Delivery Date in MTO Man...

引用

3rd International Conference on Energy, Environment and Sustainable Development (EESD 2013)

作者： Hao, Juan Yu, Jianjun Wu, Miancan South China Univ Technol Sch Business Adm Guangzhou Guangdong Peoples R China Guangdong Univ Foreign Studies Cisco Sch Informat Guangzhou Peoples R China

ISBN: (纸本)9783037859728

In order to maximize the total profit and improve the service level, based on the perspective of queuing theory, a new approach for dynamic joint decision on price and delivery date in Make-to-order (MTO) manufacturing firms using q-learning algorithm was proposed. Compared with static price and delivery date policy, the simulation results show that the proposed algorithm performs better in total profit and service level. The total profit does not increase with the growing number of accepted orders and the number of accepted orders must match the production capacity.

关键词： price/delivery date dynamic joint decision q-learning algorithm MTO manufacturing

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A UAV Dynamic Path Planning algorithm 35

A UAV Dynamic Path Planning Algorithm

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35th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Hou, Xiaojian Liu, Fei Wang, Renjie Yu, Yao Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Engn Res Ctr Ind Spectrum Imaging Beijing 100083 Peoples R China

ISBN: (纸本)9781728176840

In this paper, we propose a UAV dynamic path planning algorithm to solve the path planning problem of a single UAV in a dynamic environment. The contributions of this paper mainly include the following two folds: (1) Using a combination of global and local path planning to improve planning efficiency. (2) The improved q-learning algorithm and artificial potential field method are combined to solve the problem that effective path planning cannot be performed between two path nodes. Finally, simulation results with Matlab proves the effectiveness of the algorithm.

关键词： UAV path planning q-learning algorithm artificial potential field method

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Adaptive Inventory Control and Bullwhip Effect Analysis for Supply Chains with Non-stationary Demand 27

Adaptive Inventory Control and Bullwhip Effect Analysis for ...

引用

27th Chinese Control and Decision Conference (CCDC)

作者： Yang, Songpo Zhang, Jihui Qingdao Univ Inst Complex Sci Qingdao 266071 Peoples R China

ISBN: (纸本)9781479970162

In this paper, two adaptive inventory control models, i.e. centralized and decentralized respectively, for a multi-echelon multi-cycle supply chain consisting of one supplier and one retailer with non-stationary stochastic demand were established, In the centralized model, the vendor managed inventory replenishment policy was used by the supplier and the retailer didn't keep any stock, An improved exponential smoothing method was used by the supplier to forecast the future demand. The EOq model was used by the supplier to determine the replenishment quantity for the retailer and an adaptive approach was used by the supplier to determine his safety stock to against demand fluctuation. An reinforcement learning algorithm was adopted to select an proper safety factor according to the stochastic demand. On the contrary in the decentralized model, both the supplier and the retailers hold their own inventory and safety stock for themselves respectively. That is, they control their own inventory independently. In both cases, the aim is to satisfy the given target service level predefined. In our simulation study two types of demand patterns, stationary and non-stationary demand, are considered respectively. The bullwhip effect generated in the course of forecasting and processing of demand information were analyzed. The results show that the proposed method can satisfy the given service level and mitigate the bullwhip effect to some extent.

关键词： Vendor Managed Inventory q-learning algorithm Bullwhip Effect

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Agent-Based Simulation of Power Markets under Uniform and Pay-as-Bid Pricing Rules using Reinforcement learning

Agent-Based Simulation of Power Markets under Uniform and Pa...

引用

IEEE/PES Power Systems Conference and Exposition

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

ISBN: (纸本)9781424401772

In this paper agent-based simulation is employed to study the power market operation under two alternative pricing systems: uniform and discriminatory (pay-as-bid). Power suppliers are modeled as adaptive agents capable of learning through the interaction with their environment, following a Reinforcement learning algorithm. The SA-q-learning algorithm, a slightly changed version of the popular q-learning, is used in this paper;it proposes a solution to the difficult problem of the balance between exploration and exploitation and it has been chosen for its quick convergence. A test system with five supplier-agents is used to study the suppliers' behavior under the uniform and the pay-as-bid pricing systems.

关键词： electricity spot markets multi-agent modeling pay-as-bid q-learning algorithm uniform pricing

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Privacy-Cost Management in Smart Meters Using Deep Reinforcement learning 10

Privacy-Cost Management in Smart Meters Using Deep Reinforce...

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10th IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe) - Smart Grids - Key Enablers of a Green Power System

作者： Shateri, Mohammadhadi Messina, Francisco Piantanida, Pablo Labeau, Fabrice McGill Univ Montreal PQ Canada Univ Paris Sud CNRS Cent Supelec Gif Sur Yvette France

ISBN: (纸本)9781728171005

Smart meters (SMs) play a pivotal rule in the smart grid by being able to report the electricity usage of consumers to the utility provider (UP) almost in real-time. However, this could leak sensitive information about the consumers to the UP or a third-party. Recent works have leveraged the availability of energy storage devices, e.g., a rechargeable battery (RB), in order to provide privacy to the consumers with minimal additional energy cost. In this paper, a privacy-cost management unit (PCMU) is proposed based on a model-free deep reinforcement learning algorithm, called deep double q-learning (DDqL). Empirical results evaluated on actual SMs data are presented to compare DDqL with the state-of-the-art, i.e., classical q-learning (CqL). Additionally, the performance of the method is investigated for two concrete cases where attackers aim to infer the actual demand load and the occupancy status of dwellings. Finally, an abstract information-theoretic characterization is provided.

关键词： Smart meters privacy Privacy-cost trade-off Deep reinforcement learning q-learning algorithm Deep double q-learning Privacy-cost management unit

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Hierarchical Multi-agent System in Traffic Network Signalization with Improved Genetic algorithm

Hierarchical Multi-agent System in Traffic Network Signaliza...

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IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

作者： Tan, Min Keng Chuo, Helen Sin Ee Chin, Renee Ka Yin Yeo, Kiam Beng Teo, Kenneth Tze Kin Univ Malaysia Sabah Fac Engn Modelling Simulat & Comp Lab Kota Kinabalu Malaysia Univ Malaysia Sabah Fac Med & Hlth Sci Kota Kinabalu Malaysia

ISBN: (纸本)9781538678138

Instead of using classical offline data-driven optimization technique in traffic network signal control, this work aims to explore the potential of implementing an online data-driven optimization technique. A dynamic modeling technique is proposed using q-learning (qL) algorithm to online observe and learn the inflow-outflow traffic behaviors and extract the model parameters to update the evaluation model used in the fitness function of genetic algorithm (GA). The proposed GA with dynamic modeling is known as dyna-GA. Dyna-GA is then integrated into a hierarchical-based multi-agent traffic signal control system which consists of two layers. The lower-layer consists of several local agents that have autonomy in controlling their local intersection, whereas the upper-layer consists of one supervisory agent that has jurisdiction on all the local agents. The supervisory agent has the superiority in overwriting the local control decision if conflict occurred. The robustness of the proposed dyna-GA under several traffic scenarios is tested using a simulated arterial traffic network. The simulation results show the proposed dyna-GA has better performances in minimizing travel delay as compared to the classical GA which does not have the dynamic model.

关键词： traffic signal control multi-agent system dynamic modeling genetic algorithm q-learning algorithm

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A Reinforcement learning Approach to Dynamic Optimization of Load Allocation in AGC System

A Reinforcement Learning Approach to Dynamic Optimization of...

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General Meeting of the IEEE-Power-and-Energy-Society

作者： Wang, Y. M. Liu, q. J. Yu, T. S China Univ Technol Elect Power Coll Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781424442409

A Reinforcement learning (RL) method applied to the dynamic load allocation in AGC system is presented. The problem can be modeled as a Markov Decision Process (MDP). The q-learning algorithm as a model-free learning algorithm is introduced. It learns an optimal action strategy by experience from exploring an unknown system and getting rewards. Rewards are chosen to express how well actions control the system. The applications of the q-learning algorithm to the two-area power system model and China Southern,Power Grid model are presented. The case study shows that the q-learning algorithm enhances the performance of AGC system under CPS.

关键词： Reinforcement learning q-learning algorithm dynamic load allocation MDP CPS

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qoS-Aware Heterogeneous Networking Using Distributed Multiagent Schemes

QoS-Aware Heterogeneous Networking Using Distributed Multiag...

引用

7th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： Chen, Jiann-Liang Larosa, Yanuarius Teofilus Deng, Der-Jiunn Yang, Pei-Jia Ma, Yi-Wei Natl Changhua Univ Educ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei Taiwan Natl Cheng Kung Univ Dept Engn Sci Tainan Taiwan

ISBN: (纸本)9781424495375

This study achieves quality-of-Service (qoS) management in heterogeneous networking using a distributed multiagent scheme (DMAS) based on the concept of cooperation and the awareness algorithm. The proposed scheme is developed for supporting qoS management in a user-accepted and cost-effective fashion, which consists of a collection of problem-solving agents with three modules: the knowledge source, the in-cloud blackboard system, and the control engine built into the scheme. A set of problem-solving agents autonomously process local tasks and cooperatively interoperate via an in-cloud blackboard system to guarantee qoS. An awareness algorithm, called the q-learning algorithm, calculates the exceptive rewards of a handoff to all access networks. These rewards are then used by these problem-solving agents to determine what to do. Through operations and cooperation among the active agents, a policy is selected and a user-accepted schedule that meets the specified qoS is generated. Compared with traditional qoS management mechanisms, the proposed DMAS scheme has a 36% lower packet loss ratio in video streaming applications and a 34% lower average delay in VoIP applications with only a minor sacrifice in system computational complexity.

关键词： Heterogeneous Network Cooperative Networking Distributed Multi-Agent Scheme Cloud Computing q-learning algorithm quality of Service

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Reinforcement learning algorithms in Global Path Planning for Mobile Robot

Reinforcement Learning Algorithms in Global Path Planning fo...

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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Sichkar, Valentyn N. ITMO Univ Dept Control Syst & Robot St Petersburg Russia

ISBN: (纸本)9781538681190

The paper is devoted to the research of two approaches for global path planning for mobile robots, based on q-learning and Sarsa algorithms. The study has been done with different adjustments of two algorithms that made it possible to learn faster. The implementation of two Reinforcement learning algorithms showed differences in learning time and the methods of building path to avoid obstacles and to reach a destination point. The analysis of obtained results made it possible to select optimal parameters of the considered algorithms for the tested environments. Experiments were performed in virtual environments where algorithms learned which steps to choose in order to get a maximum payoff and reach the goal avoiding obstacles.

关键词： reinforcement learning q-learning algorithm Sarsa algorithm path planning mobile agent

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Cognitive Radio Jamming Mitigation using Markov Decision Process and Reinforcement learning

Cognitive Radio Jamming Mitigation using Markov Decision Pro...

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International Conference on Advanced Wireless Information and Communication Technologies (AWICT)

作者： Slimeni, Feten Scheers, Bart Chtourou, Zied Le Nir, Vincent Attia, Rabah Mil Acad Tunisia VRIT Lab Nabeul 8000 Tunisia Royal Mil Acad CISS Dept B-1000 Brussels Belgium EPT Univ Carthage SERCOM Lab Marsa 2078 Tunisia

The Cognitive radio technology is a promising solution to the imbalance between scarcity and under utilization of the spectrum. However, this technology is susceptible to both classical and advanced jamming attacks which can prevent it from the efficient exploitation of the free frequency bands. In this paper, we explain how a cognitive radio can exploit its ability of dynamic spectrum access and its learning capabilities to avoid jammed channels. We start by the definition of jamming attacks in cognitive radio networks and we give a review of its potential countermeasures. Then, we model the cognitive radio behavior in the suspicious environment as a markov decision process. To solve this optimization problem, we implement the q-learning algorithm in order to learn the jammer strategy and to pro-actively avoid jammed channels. We present the limits of this algorithm in cognitive radio context and we propose a modified version to speed up learning a safe strategy. The effectiveness of this modified algorithm is evaluated by simulations and compared to the original q-learning algorithm. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： Cognitive radio network jamming attack q-learning algorithm

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