论文标题
对IEEE的教练任务的强化学习分析非常小的足球
An analysis of Reinforcement Learning applied to Coach task in IEEE Very Small Size Soccer
论文作者
论文摘要
IEEE尺寸很小的足球（VSSS）是一场机器人足球比赛，其中两个小型机器人组成的两支球队相互对抗。传统上，确定性教练代理人将为每个对手的策略选择最合适的策略和形成。因此，教练的角色对比赛非常重要。从这个意义上讲，本文提出了一种基于强化学习（RL）的教练任务的端到端方法。提出的系统在模拟匹配项期间处理信息，以学习选择当前编队的最佳策略，具体取决于对手和游戏条件。我们在模拟的环境中针对三个不同的团队（平衡，进攻和激烈的进攻）培训了两项RL政策。我们的结果是针对VSSS联赛顶级球队之一的评估，在达到约2.0的双赢率后，结果表现出了令人鼓舞的成绩。
The IEEE Very Small Size Soccer (VSSS) is a robot soccer competition in which two teams of three small robots play against each other. Traditionally, a deterministic coach agent will choose the most suitable strategy and formation for each adversary's strategy. Therefore, the role of a coach is of great importance to the game. In this sense, this paper proposes an end-to-end approach for the coaching task based on Reinforcement Learning (RL). The proposed system processes the information during the simulated matches to learn an optimal policy that chooses the current formation, depending on the opponent and game conditions. We trained two RL policies against three different teams (balanced, offensive, and heavily offensive) in a simulated environment. Our results were assessed against one of the top teams of the VSSS league, showing promising results after achieving a win/loss ratio of approximately 2.0.
