English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 96274/126892 (76%)
Visitors : 32311338      Online Users : 307
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 理學院 > 資訊科學系 > 會議論文 >  Item 140.119/135513
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/135513

    Title: A Study on the Effectiveness of A2C and A3C Reinforcement Learning in Parking Space Search in Urban Areas Problem
    Authors: 張宏慶
    Jang, Hung-Chin
    Huang, Yi-Chen
    Chiu, Hsien-An
    Contributors: 資科系
    Keywords: A2C;A3C;parking space search;reinforcement learning
    Date: 2020-10
    Issue Date: 2021-06-04 14:35:31 (UTC+8)
    Abstract: Reinforcement learning (RL) helps to select a strategy to execute by gradually predicting and learning according to the reward or punishment feedback given by the environment after selecting a particular strategy to optimize the benefits. The advantage of this model-free method is that it does not need to understand the environment, nor does it take a long time to build a model, but based on what the environment gives, wait for feedback, and take the next step based on the feedback. Reinforcement learning is also suitable for immediate problem-solving applications. This research uses reinforcement learning to solve the problem of searching for parking spaces in urban areas quickly. The proposed method only needs to set up sensors at the road intersections to sense the vehicles and count the number of vehicles passing through, and the probability of parking vacancy can be estimated based on the length of the road and the number of vehicles entering and exiting the road in a specific time interval. Then through the evaluation results of the policy-based A2C (Advantage Actor-Critic) and A3C (Asynchronous Advantage Actor-Critic), it provides vehicles with the most likely parking routes suggestions. This research uses the traffic flow and parking information of each time period in the road segment of the Taipei city. At last, we compare the expected searching time of A2C and A3C reinforcement learning in the parking space search problem in urban areas.
    Relation: Proceeding of the 11th International Conference on ICT Convergence (ICTC2020), KICS, IEEE ComSoc, IEICE Communications Society
    Data Type: conference
    DOI 連結: https://doi.org/10.1109/ICTC49870.2020.9289269
    DOI: 10.1109/ICTC49870.2020.9289269
    Appears in Collections:[資訊科學系] 會議論文

    Files in This Item:

    File Description SizeFormat
    273.pdf1612KbAdobe PDF12View/Open

    All items in 政大典藏 are protected by copyright, with all rights reserved.

    社群 sharing

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback