Robocup2D项目中Agent2D底层动作链机制的分析优化

Analysis and Optimization of the Action Chain Mechanism in Agent2D Underlying in Robo Cup2D Soccer League

在Robo Cup2D仿真足球项目中,Agent2D是我国使用最为广泛的球队底层之一。仿真平台中数据传输的噪声干扰及代码自身动作链机制不完整等因素,导致采用Agent2D底层的球队在应对不同的队伍时,存在着适应能力不足的缺点,影响了球队的整体能力。该论文引入了动作修正参数,利用强化学习的手段对动作链机制进行优化,使Agent底层球队在面对不同风格的对手时可以选择更加有效的动作执行,以此来提升球队的适应性。仿真实验证明,此法具有一定效果。

In the RoboCup2D soccer league, Agent2D is one of the most widely used underlying team in China. Data transmission noise and the incomplete action chain mechanism make the underlying teams using Agent2D be lack of flexibility. This paper introduces an action correcting parameter and optimizes the operation of the action chain by reinforcement learning mechanism. The performance of the Agent2D underlying team is improved in the game and the adaptability of the team is enhanced. Simulation experiment results show that this method has a certain effect.