Algorithmic-Game-Theory-Application-on-Multi-agent-Combat-and-Verification-Platf
本科毕业设计:《多智能体博弈兵棋推演理论与验证平台设计》的源代码附录内容。强化学习算法的实现上参考了周沫凡先生的开源代码https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow
文件说明： 一、算法代码文件夹内容： 附录A: cal_matrix.py 是仅限1v1情形下计算“normal form”的支付矩阵中的每一个元素的值的代码， 用于一致性检验，例如 情形下其输出为一个6x6矩阵。 附录B: LH.py是使用LH源代码的matlab代码的接口文件，由于LH源代码在健壮性和格式统一上有所不足， 因而编写该文件方便进行调用。 bimat.m即为LH源代码的matlab文件。 bimat_zero.m是适应零和博弈的情形的接口改动，LH.py实际直接调用的文件为此文件。 nash_recurrence.py是最初只适应于1v1的子博弈递推计算的代码。 nash_recurrence_2_2.py是在nash_recurrence.py基础上实现了双方智能体的扩展， 泛用于m v n 的情形，功能上可以完全取代nash_recurrence.py。 附录D: 强化学习代码相关文件。 包括DQN、NashQLearning两个文件中的强化学习代码以及trained_vs_DQN_main.py，shoot_env.py是训练环境代码， 其它代码： draw_plot.py，主要是绘图和数据统计等功能性函数。 二、验证平台代码文件夹内容： 附录C…
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