The Application of Co-evolutionary Genetic Programming and TD(1) Reinforcement Learning in Large-Scale Strategy Game VCMI

被引:3
|
作者
Wilisowski, Lukasz [1 ]
Drezewski, Rafal [1 ]
机构
[1] AGH Univ Sci & Technol, Dept Comp Sci, Krakow, Poland
来源
AGENT AND MULTI-AGENT SYSTEMS: TECHNOLOGIES AND APPLICATIONS | 2015年 / 38卷
关键词
Genetic programming; Neural networks; Strategy games; CARLO TREE-SEARCH;
D O I
10.1007/978-3-319-19728-9_7
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
VCMI is a new, open-source project that could become one of the biggest testing platform for modern AI algorithms in the future. Its complex environment and turn-based gameplay make it a perfect system for any AI driven solution. It also has a large community of active players which improves the testability of target algorithms. This paper explores VCMI's environment and tries to assess its complexity by providing a base solution for battle handling problem using two global optimization algorithms: Co-Evolution of Genetic Programming Trees and TD(1) algorithm with Back Propagation neural network. Both algorithms have been used in VCMI to evolve battle strategies through a fully autonomous learning process. Finally, the obtained strategies have been tested against existing solutions and compared with players' best tactics.
引用
收藏
页码:81 / 93
页数:13
相关论文
共 50 条
  • [21] Co-evolutionary competitive swarm optimizer with three-phase for large-scale complex optimization problem
    Huang, Chen
    Zhou, Xiangbing
    Ran, Xiaojuan
    Liu, Yi
    Deng, Wuquan
    Deng, Wu
    INFORMATION SCIENCES, 2023, 619 : 2 - 18
  • [22] CCFR2: A more efficient cooperative co-evolutionary framework for large-scale global optimization
    Yang, Ming
    Zhou, Aimin
    Li, Changhe
    Guan, Jing
    Yan, Xuesong
    INFORMATION SCIENCES, 2020, 512 : 64 - 79
  • [23] Effective Resource Allocation in Cooperative Co-evolutionary Algorithm for Large-Scale Fully-Separable Problems
    Du, Wei
    Tong, Le
    Tang, Yang
    2020 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2020, : 4198 - 4203
  • [24] Formal Concept Analysis based Grouping Co-Evolutionary Optimization Algorithms for Large-Scale Global Optimization
    Ma L.-B.
    Chang F.-R.
    Zhang H.-X.
    Wang X.-W.
    Huang M.
    Hao F.
    Jisuanji Xuebao/Chinese Journal of Computers, 2021, 44 (07): : 1310 - 1325
  • [25] The Application of an Evolutionary Programming Process to a Simulation of the ETEX Large-Scale Airborne Dispersion Experiment
    Werth, David
    Maze, Grace
    Buckley, Robert
    Chiswell, Steven
    JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY, 2019, 58 (03) : 511 - 525
  • [26] An Interactive Co-Evolutionary Framework for Multi-Objective Critical Node Detection on Large-Scale Complex Networks
    Zhang, Lei
    Zhang, Huaijin
    Yang, Haipeng
    Liu, Zhengyi
    Cheng, Fan
    IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 2023, 10 (03): : 1722 - 1735
  • [27] INTEGRATING LARGE-SCALE ONTOLOGIES FOR ECONOMIC AND FINANCIAL SYSTEMS VIA ADAPTIVE CO-EVOLUTIONARY NSGA-II
    Xue, Xingsi
    Tan, Wenbin
    Lv, Jianhui
    FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY, 2023, 31 (06)
  • [28] Nonzero-Sum Game Reinforcement Learning for Performance Optimization in Large-Scale Industrial Processes
    Li, Jinna
    Ding, Jinliang
    Chai, Tianyou
    Lewis, Frank L.
    IEEE TRANSACTIONS ON CYBERNETICS, 2020, 50 (09) : 4132 - 4145
  • [29] An efficient evolutionary algorithm based on deep reinforcement learning for large-scale sparse multiobjective optimization
    Gao, Mengqi
    Feng, Xiang
    Yu, Huiqun
    Li, Xiuquan
    APPLIED INTELLIGENCE, 2023, 53 (18) : 21116 - 21139
  • [30] Evolutionary reinforcement learning algorithm for large-scale multi-agent cooperation and confrontation applications
    Liu, Haiying
    Li, ZhiHao
    Huang, Kuihua
    Wang, Rui
    Cheng, Guangquan
    Li, Tiexiang
    JOURNAL OF SUPERCOMPUTING, 2024, 80 (02): : 2319 - 2346