An analysis of Reinforcement Learning applied to Coach task in IEEE Very Small Size Soccer
This work addresses the problem of dynamic strategy selection for robot soccer teams, offering a data-driven alternative to traditional deterministic coach agents.
This paper explores an end-to-end Reinforcement Learning (RL) approach for the coaching task in IEEE Very Small Size Soccer (VSSS). The system learns to select optimal formations based on opponent and game conditions, achieving a win/loss ratio of approximately 2.0 against a top VSSS team.
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.