Based on soccer robot simulation as its research platform, this paper studies the learning of high level strategy of multi-agent adversarial system.
本文以足球仿真机器人系统为研究平台，研究多智能体对抗系统的高层策略学习问题。
By the simulation of a sample path, and the approximation to performance potentials via neural networks based on reinforcement learning (RL), the systems optimization methods are provided.
在样本轨道仿真的基础上，利用神经网络进行强化学习仿真逼近系统的性能势，进而对系统进行优化。
The simulation results show that ICSA based on this method of mutation can improve the rapidity of learning BP network well and avoid prematurity effectively.
仿真实验表明，基于这种变异方法的免疫克隆选择算法可以很好地提高BP网络的学习速度，有效地避免算法过早收敛的问题。
Simulation results show that the optimal selection approach based on PSO is available and the PSO-SVR model has superior learning accuracy and generalization performance.
仿真结果表明：该PSO优化SVR参数方法可行、有效，由此得到的SVR模型具有更好的学习精度和推广能力。
The result of simulation illustrates that the signal control method based on Q-Learning is better than fixed-time control, actuated control and signal control based on genetic algorithms.
仿真实验的结果表明，基于Q -学习的信号控制方法优于定时控制、感应式控制和基于遗传算法的信号控制方法。
Controller is designed based on VHDL, the research keys are the implementation of the on-line learning algorithm of CMAC and the closed-loop simulation test of the controller.
基于VHDL设计该控制器，重点在于CMAC的在线学习算法实现和控制器模块的闭环仿真测试。
Based on the infrared feature database of the scene, a machine learning algorithm is applied to realize the IR scene real-time simulation.
为实现红外场景实时仿真，以场景的红外特性数据库为基础，将机器学习算法引入到仿真之中。
Based on the infrared feature database of the scene, a machine learning algorithm is applied to realize the IR scene real-time simulation.
为实现红外场景实时仿真，以场景的红外特性数据库为基础，将机器学习算法引入到仿真之中。
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