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检索条件"主题词=Q-Learning algorithm"

共 199 条记录，以下是161-170 订阅

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Reinforcement learning algorithms in Global Path Planning for Mobile Robot

Reinforcement Learning Algorithms in Global Path Planning fo...

引用

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...

引用

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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Dynamic Task Allocation for Formation Air-to-ground Attack

Dynamic Task Allocation for Formation Air-to-ground Attack

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6th International Conference on Advanced Computational Intelligence (ICACI)

作者： Zhang, An Guo, Fengjuan Northwestern Polytech Univ Fremont CA 94539 USA

ISBN: (纸本)9781467363433

In accordance with the allocation of random arriving tasks, queuing network is proposed and then utilized to establish combat model for air-to-ground attack of formation. With the methodology of Markov Decision Processes (MDP), the paper establishes the dynamic task assignment model under the complete information condition. Allowing for the issue of dynamic task allocation with incomplete information, load threshold and value function sharing mechanism are proposed. Afterwards, two layers q-learning algorithm is presented in this context to establish the dynamic task allocation model, which aims to achieve the cooperation with in formation and between formations. The simulation result indicates that the model aforementioned can enhance the long-term profit value, as well as avoid omitting the target with bigger income. Furthermore, it sheds light on the study on the dynamic task allocation and combat effectiveness evaluation for air-to-ground attack of formation problem in theory and method.

关键词： Formation Dynamic Task Allocation MDP q-learning algorithm

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Enhancing Student Engagement in Smart Classrooms Using Reinforcement learning algorithms

Enhancing Student Engagement in Smart Classrooms Using Reinf...

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2024 Cross Strait Radio Science and Wireless Technology Conference

作者： Li, Yantao Wei, Ji Guangdong Univ Sci & Technol Guangzhou Peoples R China

ISBN: (纸本)9798331507800;9798331507794

This study aims to dynamically optimize the teaching strategy in smart classrooms by applying the q-learning reinforcement learning algorithm to improve students' classroom participation. This paper designs a complete teaching feedback system that can collect students' classroom behavior data in real time and dynamically adjust the teaching strategy based on the q-learning algorithm to improve the teaching effect. The experiment verifies the application effect of the q -learning algorithm in smart classrooms by simulating three different teaching scenarios: large-class teaching, group cooperation, and personalized tutoring. The experimental results show that the q-learning algorithm can effectively improve students' participation, especially in the group discussion and personalized tutoring modes, where the students' participation rate increases from 50% in the initial stage to 85%. In contrast, although the participation rate of the lecture mode increases less, it maintains a relatively stable growth. The research results further prove the effectiveness of the q -learning algorithm in optimizing teaching strategies in different classroom scenarios and demonstrate its potential for wide application in smart education systems. In addition, this paper discusses the limitations of the study and possible future research directions, and points out that more deep learning algorithms, such as deep q networks (DqN) or long short-term memory networks, can be introduced to enhance the intelligence level of the system and further optimize the personalized learning path.

关键词： q-learning algorithm smart classroom teaching strategy optimization student engagement personalized learning deep learning

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Car-Following Safe Headway Strategy with Battery-Health Conscious: A Reinforcement learning Approach

Car-Following Safe Headway Strategy with Battery-Health Cons...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Jia, Xi Peng, Jun Liu, Yongjie Liu, Bo Wang, Pingping Lu, Yao Wen, Mengfei Huang, Zhiwu Cent South Univ Sch Automat Changsha Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China Hunan Engn Lab Rail Vehicles Braking Technol Changsha Peoples R China Changsha Coll Presch Educ Changsha Peoples R China

ISBN: (纸本)9781728185262

This paper proposes an optimal car-following strategy for pure electric vehicles (EVs) with the aim of keeping an expected headway of the leader and reducing vehicle battery loss. In particular, a car-following system model is established. The primary task of the automatic vehicle is to follow the trajectory of the preceding car and maintain an expected headway. Then, the paper analyzes the powertrain of the electric vehicle. The loss of battery life over a period of time is proportional to the acceleration, so it takes the battery life into consideration. The q-learning algorithm is conducted for the optimal car-following strategy using system data instead of system dynamics information. It utilizes reward function and greedy strategy to select actions to train the following vehicle to achieve car-following safety. When there is no collision in these two cars, acceleration is considered into reward function to reduce battery loss. Finally, it is verified by simulation that the proposed car-following strategy can keep good tracking, maintain the expected headway from the preceding vehicle, and reduce battery loss.

关键词： Car-following system q-learning algorithm safe headway battery loss

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Multi-path Transmission Strategy for Deterministic Networks 13th

Multi-path Transmission Strategy for Deterministic Networks

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13th International Conference on Computer Engineering and Networks (CENet)

作者： Zheng, Fei Li, Kelin Zhou, Zou Hu, Yu Chen, Longjie Guilin Univ Elect Technol Minist Educ Key Lab Cognit Radio & Informat Proc Guilin 541004 Peoples R China

ISBN: (纸本)9789819992492;9789819992478;9789819992461

With the rapid growth of internet traffic, network congestion becomes more and more severe, which causes massive packets loss. The reliability and delay of data transmission needs to be guaranteed in real-time applications such as financial transactions and cloud games. Traditional transmission strategies use packet retransmission to ensure data reliability, but the retransmission causes extra delay. The extra delay reduces the quality of service (qoS) for deterministic services. For the above problem, this paper proposes a deterministic network (DetNet)-oriented multipath transmission strategy in the software-defined network (SDN) architecture. The architecture introduces the packet replication and elimination function (PREF) of DetNet to achieve reliable transmission. The strategy establishes a path optimization model with transmission delay and packet loss rate, and then solve the model by the q-learning algorithm. The delay and packet loss rate of the link construct the reward function. The q-value table enables to obtain the best combination of paths, and it is gained by the reward function. Simulations show that our strategy have lower packet loss rate and delay jitter than the traditional single-path transmission strategy and the multi-path transmission strategy.

关键词： Software-defined networking (SDN) Deterministic network q-learning algorithm Packet loss rate Delay jitter

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learning policies for single machine job dispatching

Learning policies for single machine job dispatching

引用

13th International Conference on Flexible Automation and Intelligent Manufacturing

作者： Wang, YC Usher, JM Kun Shan Univ Technol Dept Informat Management Tainan 710 Taiwan Mississippi State Univ Dept Ind Engn Mississippi State MS 39762 USA

Reinforcement learning (RL) has received some attention in recent years from a,gent-based researchers because it deals with the problem of how an autonomous agent can learn to select proper actions for achieving its goals through interacting with its environment. Each time after an agent performs an action, the environment's response, as indicated by its new state, is used by the agent to reward or penalize its action. The agent's goal is to maximize the total amount of reward it receives over the long run. Although there have been several successful examples demonstrating the usefulness of RL, its application to manufacturing systems has not been fully explored. In this study, a single machine agent employs the q-learning algorithm to develop a decision-making policy on selecting the appropriate dispatching rule from among three given dispatching rules. The system objective is to minimize mean tardiness. This paper presents a factorial experiment design for studying the settings used to apply q-learning to the single machine dispatching rule selection problem. The factors considered in this study include two related to the agent's policy table design and three for developing its reward function. This study not only investigates the main effects of this q-learning application but also provides recommendations for factor settings and useful guidelines for future applications of q-learning to agent-based production scheduling. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： reinforcement learning q-learning algorithm dispatching rule selection

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High-speed Train Timetabling Based on Reinforcement learning

High-speed Train Timetabling Based on Reinforcement Learning

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Yang, Wanlu Jiang, Peng Song, Shiji Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Univ BNRist Beijing 100084 Peoples R China

ISBN: (纸本)9781665487689

Chinese high-speed railway has developed rapidly in the more intelligent and automatic direction over the past few decades. In this paper, we consider the optimization problem of the train timetable for the high-speed railway to minimize the total train waiting time and total station occupied time. To deal with time-related constraints, we first establish the train operation environment based on Discrete Event Dynamic System (DEDS). Then, we reformulate the timetabling problem as a Markov Decision Process (MDP) problem and propose an improved q-learning approach by redesigning q-value function to solve the problem. Finally, we consider the Beijing-Shanghai high-speed railway as a numerical example, where the passenger flow and train running time are stochastic. We empirically show that our q-learning method reduces over 30% total waiting time and 1.9% total occupied time compared with the well-known First-Come-First-Service (FCFS) scheduling strategy.

关键词： High-speed railway Train timetable Markov decision process q-learning algorithm

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An Analysis of Market Mechanism and Bidding Strategy for Power Balancing Market in Micro-grid

An Analysis of Market Mechanism and Bidding Strategy for Pow...

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China International Conference on Electricity Distribution (CICED)

作者： Jie, Bo Tsuji, Takao Yokohama Natl Univ Yokohama Kanagawa Japan

ISBN: (纸本)9781467390682

With the penetration of renewable energy sources such as photovoltaics or wind power, it becomes a more important issue to keep power system stable in terms of power balance with proper frequency. To this end, balancing markets have been developed and utilized in order to ensure the security of frequency regulation with lower cost based on the market principle. For example, in the European countries and regions, secondary and tertiary control balancing energy have been traded through balancing markets. Although the details about those market mechanisms are different from country to country, there are two mainstreams: "uniform-price auction" based on marginal fuel cost and "pay-as-bid auction" with discriminatory pricing. Under the uniform-price auctions, all players whose biddings are cleared receive the same market-clearing price which is the highest offer price cleared. In contrast, in the case of pay-as-bid auction, prices paid to cleared players are based on their own actual discriminatory bidding price, rather than the highest clearing price. For power suppliers, one of the most important issues is how to determine the bidding strategy in the electricity balancing auction market to satisfy their profit maximizing goals. In this paper, supposing the frequency regulation in Micro-grid will be also realized based on balancing market, fundamental characteristics of two auction mechanisms, uniform-price and pay-as-bid methods, were examined. The behavior of each player in the balancing market, namely bidding strategy, is modelled by using q-learning algorithm to maximize player's profit. In this approach, the bidding strategy is regarded as one kind of stochastic optimal control problems and agent-based simulations are performed in which each player can learn from past experience. The performance comparison of the balancing market is discussed based on the simulation results.

关键词： balancing market market mechanism bidding strategy uniform-pricing auction pay-as-bid auction q-learning algorithm micro-grid

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Aero-engine life limit parts replacement policy optimization: Reinforcement learning method

Aero-engine life limit parts replacement policy optimization...

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International Symposium on Advanced Reliability and Maintenance Modeling (APARM)

作者： Lin, Lin Liu, Jie Liu, Jinshan Zhong, Shisheng Guo, Feng Harbin Inst Technol Sch Mechatron Engn Harbin Peoples R China China Aerosp Sci & Technol Corp Beijing Spacecrafts Beijing Peoples R China

ISBN: (纸本)9781728171029

An optimization method for aero-engine life limit parts (LLPs) replacement policy is proposed based on reinforcement learning method, aiming at optimizing the aero-engine LLPs replacement policy. In the proposed LLPs replacement policy optimization method, the real-life LLPs replacement rules are adopted as the constraints and the minimum long-term LLPs replacement discount cost is regarded as the optimization objective. In reinforcement learning framework, the q-learning algorithm is adopted to optimize the LLPs replacement policy. Compared with the traditional methods, the proposed optimization method is simple in structure, and it can achieve better optimization results. To validate the proposed aero-engine LLPs replacement policy optimization method, the LLPs list of a civil turbofan aero-engine is adopted as the sample data. And the existing particle swam optimization algorithm is adopted as the comparative experimental method. The comparison experiment results show that the proposed LLPs replacement policy optimization method achieves obvious advantages. The proposed optimization method is able to provide decision-making supports for aero-engine LLPs replacement.

关键词： aero-engine life limit part replacement policy reinforcement learning q-learning algorithm

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