Development of a Simulation Environment for Robot Soccer Game with Deep Reinforcement Learning and Role Assignment

The robot soccer game has been recognized as an excellent scenario to test the gaming algorithm of multi-agent systems. This paper develops a new simulation platform for the robot soccer game, and it has the advantage of open architecture, such that the formation control scheme, the path planning strategy for multiple robots, and many other algorithms can be implemented and tested. Specifically, both a Deep Reinforcement Learning (DRL) scheme and a role-assignment-based method have been successfully realized in this platform to drive multiple robots to play the soccer game, including 2V2,3V3,4V4, and so on. It is believed that the developed simulation environment can be used for data collection and transfer learning (TL), hence bridging the gap of Sim2Real technique in actual implementations.