Learning to Play Soccer by Reinforcement and Applying Sim-to-Real to Compete in the Real World
Learning to Play Soccer by Reinforcement and Applying Sim-to-Real to Compete in the Real World
著者: Hansenclever F. Bassani, Renie A. Delgado, Jose Nilton de O. Lima Junior, Heitor R. Medeiros, Pedro H. M. Braga, Alain Tapp
分類: cs.LG, cs.AI, cs.RO, stat.ML
原文アブストラクト
This work presents an application of Reinforcement Learning (RL) for the complete control of real soccer robots of the IEEE Very Small Size Soccer (VSSS), a traditional league in the Latin American Robotics Competition (LARC). In the VSSS league, two teams of three small robots play against each other. We propose a simulated environment in which continuous or discrete control policies can be trained, and a Sim-to-Real method to allow using the obtained policies to control a robot in the real world. The results show that the learned policies display a broad repertoire of behaviors that are difficult to specify by hand. This approach, called VSSS-RL, was able to beat the human-designed policy for the striker of the team ranked 3rd place in the 2018 LARC, in 1-vs-1 matches.