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    • Thesis & Report, BSc (Computer Science and Engineering)
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    •   BracU IR
    • School of Engineering and Computer Science (SECS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Self-learning game bot using deep reinforcement learning

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    14301050_CSE.pdf (1.230Mb)
    Date
    2017-12
    Publisher
    BRAC University
    Author
    Ananto, Azizul Haque
    Metadata
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    URI
    http://hdl.handle.net/10361/9509
    Abstract
    We present a deep learning model for playing games with high level input (image/raw pixel) using reinforcement learning. The games are action limited (like snakes, catcher, air-raider etc.). The model consists of convolution neural network for processing image inputs and fully connected layers for estimating actions according to the inputs where the idea of taking action is based on Q-learning (model-free reinforcement learning), yet modified it for our policy and usage. We applied our method on the python’s ‘PyGame Learning Environment’ and some other classic control games. We found our method learns fast enough but not with best accuracy. Then we tried the batch of input method which results a high score for the Catcher environment. It produced better performance in terms of the learning speed and accuracy.
    Keywords
    Game; Bot; Self-learning; Reinforcement learning
     
    Description
    This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.
     
    Cataloged from PDF version of thesis report.
     
    Includes bibliographical references (pages 45-47).
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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