臺灣博碩士論文加值系統

在本論文中，我們提出了物聯網的環境下基於強化學習的電梯排程系統．為了減少等待時間，使用者在電梯外叫電梯時，我們用Q學習的方法來分配給特定的電梯．我們把電梯跟人的狀態分了18個情況，設計了獎勵函數。模擬時我們用了16層樓跟32層樓，4台車廂的情況，訓練了上班尖峰，下班尖峰，中間尖峰，一整天的四種模型，然後跟我們實驗室之前做的減少耗能的方法比較等待時間、移動時間、耗能。我們最後得到在16層樓的上班時間的情況減少了39.5%的等待時間，32層樓的上班時間的情況減少了39.4%的等待時間。

We propose new elevator scheduling system using Q learning in IoTtalk which is a IoT platform. For reducing waiting time, when a user calls an elevator, our system assigns specific car to the user using Q-learning method. We categorize user and state situation into 18 cases and design a new reward function. We use 16-floor and 32-floor building, with 4 cars, in simulation. We train 4 models: up peak, down peak ,inter floor and all day Compared with the energy saving algorithm developed in our lab, the average waiting time can be reduced by 39.5% in 16-floor for up peak situation and 39.4% in 32-floor for up peak situation.

摘要 ｉ
ABSTRACT ii
誌謝 iii
CONTENTS iv
LIST OF TABLES vi
LIST OF FIGURES vii
Chapter 1 Introduction 1
1.1 Motivation 2
1.2 Thesis Organization 2
Chapter 2 Background and Related Work 4
2.1 Travel Pattern 4
2.1 Related Work 4
2.3 IoTtalk Introduction 6
2.4 ElevatorTalk 6
2.5 Reinforcement Learning Introduction 8
2.5.1 Q-Learning 9
Chapter 3 Proposed method 10
3.1 Panel 10
3.2 Scheduler 12
3.3 Assign Car 13
3.3.1 Create state 13
3.3.1 Update Q-table 14
3.4 Car Schedule 16
3.5 Car 18
3.6 Design Reward Function 21
Chapter 4 Simulation and Result 23
4.1 Environment 23
4.1 Result 24
Chapter 5 Conclusion and Future Work 26
Appendix 27
Appendix A 27
Bibliography 29
Biography 30

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