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.

中文翻译：

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强化学习应用于IEEE超小型足球教练任务的分析

IEEE超小型足球（VSSS）是一项机器人足球比赛，其中两个三人一组的小型机器人相互比赛。传统上，确定性教练代理将为每个对手的策略选择最合适的策略和组成。因此，教练的角色对比赛至关重要。从这个意义上讲，本文提出了一种基于强化学习（RL）的端到端的教练任务方法。拟议的系统在模拟比赛期间处理信息，以学习根据对手和比赛条件选择当前阵型的最佳策略。我们在模拟环境中针对三个不同的团队（平衡，进攻和重进攻）训练了两个RL策略。我们的结果是根据VSSS联赛顶级球队之一进行评估的，