- Tài khoản và mật khẩu chỉ cung cấp cho sinh viên, giảng viên, cán bộ của TRƯỜNG ĐẠI HỌC FPT
- Hướng dẫn sử dụng: Xem Video .
- Danh mục tài liệu mới: Tại đây .
- Đăng nhập : Tại đây .
SỐ LƯỢT TRUY CẬP


accurate visitors web counter
Visits Counter
FPT University|e-Resources > Đồ án tốt nghiệp (Dissertations) > Khoa học máy tính >
Please use this identifier to cite or link to this item: /handle/123456789/3326

Title: TUNING PROXIMAL POLICY OPTIMIZATION ALGORITHM IN MAZE SOLVING WITH ML-AGENTS
Authors: Phan, Duy Hung
Mac, Duy Dan Truong
Phan, Thanh Hung
Keywords: Computer Science
ALGORITHM
ML-AGENTS
TUNING PROXIMAL POLICY
Issue Date: 2022
Publisher: FPTU Ha Noi
Abstract: The proximal Policy Optimization algorithm is the ML-Agents toolkit's default reinforcement algorithm. This approach can switch between sampling data via interaction with the environment and utilizing stochastic gradient descent to optimize a "surrogate" cost function. Although when creating a new machine learning model, it is tough to know the optimal model architecture for a given project immediately. In most cases, We can either utilize the algorithm's default values or we may use the machine to undertake this exploration and automatically select the best model architecture. Hyperparameters define the model architecture; thus, searching for the best model is called hyperparameter tuning. We focus on comparing four hyperparameters: Beta, Epsilon, Lambd, Num_epoch of PPO algorithm in solving a maze. The results obtained in the training process show the difference in the selection of hyperparameters. The modification of hyperparameters will depend on the maze's complexity and the complexity of the Agent's actions. This thesis will help to make appropriate choices at hyperparameters in concrete and practical projects. Code is available at hungpt17102k/Maze-Solving-ML-Agent (github.com).
URI: /handle/123456789/3326
Appears in Collections:Khoa học máy tính

Files in This Item:

File Description SizeFormat
Slides - Tuning Proximal Policy Optimization Algorithm.pdfFree4.32 MBAdobe PDF book.png
View/Open
Thesis - Tuning Proximal Policy Optimization Algorithm.pdfFree2 MBAdobe PDF book.png
View/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

  Collections Copyright © FPT University

FSE Hoa Lac Library

Add : Room 107, 1st floor, Hoa Lac campus, Km28 Thang Long Avenue, Hoa Lac Hi-Tech Park

Office tel: + 844.66805912  / Email :  thuvien_fu_hoalac@fpt.edu.vn

 - Feedback