Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89882
Title: Autonomous agents in snake game via deep reinforcement learning
Authors: Wei, Zhepei
Wang, Di
Zhang, Ming
Tan, Ah-Hwee
Miao, Chunyan
Zhou, You
Keywords: Deep Reinforcement Learning
Snake Game
Engineering::Computer science and engineering
Issue Date: 2018
Source: Wei, Z., Wang, D., Zhang, M., Tan, A.-H., Miao, C., & Zhou, Y. (2018). Autonomous agents in snake game via deep reinforcement learning. 2018 IEEE International Conference on Agents (ICA). doi:10.1109/AGENTS.2018.8460004
Abstract: Since DeepMind pioneered a deep reinforcement learning (DRL) model to play the Atari games, DRL has become a commonly adopted method to enable the agents to learn complex control policies in various video games. However, similar approaches may still need to be improved when applied to more challenging scenarios, where reward signals are sparse and delayed. In this paper, we develop a refined DRL model to enable our autonomous agent to play the classical Snake Game, whose constraint gets stricter as the game progresses. Specifically, we employ a convolutional neural network (CNN) trained with a variant of Q-learning. Moreover, we propose a carefully designed reward mechanism to properly train the network, adopt a training gap strategy to temporarily bypass training after the location of the target changes, and introduce a dual experience replay method to categorize different experiences for better training efficacy. The experimental results show that our agent outperforms the baseline model and surpasses human-level performance in terms of playing the Snake Game.
URI: https://hdl.handle.net/10356/89882
http://hdl.handle.net/10220/49389
DOI: 10.1109/AGENTS.2018.8460004
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/AGENTS.2018.8460004
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

Files in This Item:
File Description SizeFormat 
ICA2018SnakeGame.pdf315.46 kBAdobe PDFThumbnail
View/Open

Page view(s)

305
Updated on Jun 30, 2022

Download(s) 5

476
Updated on Jun 30, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.