After watching AlphaGo a Netflix documentary which presents how AlphaGo is an AI computer game developed by deep-mind technologies based on deep reinforcement learning (DRL). Since then, my interest in reinforcement learning has been growing. In this project, I will apply reinforcement learning to develop an agent to play snake game. Where Deep learning will implement a neural Network to help the agent (snake) to learn what action must take to get a state. If we describe deep reinforcement learning (DRL) model where agent interacts with an environment and chooses an action. Based on action, agents receive feedback from the environment as states (or perceives) and rewards. A state = an array with 11 input values, each input values represent a neural network that provides an output of 3 values, each one represents three possible actions the agent (snake) can take (Straight, RightTurn andLeftTurn).


    Access

    Download


    Export, share and cite



    Title :

    A Deep Reinforcement Learning Agent for Snake Game


    Contributors:

    Publication date :

    2023-12-27


    Remarks:

    doi:10.36079/lamintang.ijai-01002.565
    International Journal of Artificial Intelligence; Vol 10 No 2: December 2023; 92-102 ; 2686-3251 ; 2407-7275 ; 10.36079/lamintang.ijai-01002



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




    Multi-Agent Deep Reinforcement Learning in Vehicular OCC

    Islam, Amirul / Musavian, Leila / Thomos, Nikolaos | IEEE | 2022


    Cloud game computing offload based on Multi-Agent Reinforcement Learning

    Tian, Kaicong / Liu, Yitong / Yang, Hongwen et al. | IEEE | 2022



    Automatic Itinerary Planning Using Triple-Agent Deep Reinforcement Learning

    Chen, Bo-Hao / Han, Jin / Chen, Shengxin et al. | IEEE | 2022