Learning Skills for Small Size League RoboCup

Abstract

In this work, we show how modern deep reinforcement learning (RL) techniques can be incorporated into an existing Skills, Tactics, and Plays (STP) architecture. STP divides the robot behavior into a hand-coded hierarchy of plays, which coordinate multiple robots, tactics, which encode high level behavior of individual robots, and skills, which encode low-level control of pieces of a tactic. The CMDragons successfully used an STP architecture to win the 2015 RoboCup competition. The skills in their code were a combination of classical robotics algorithms and human designed policies. In this work, we use modern deep RL, specifically the Deep Deterministic Policy Gradient (DDPG) algorithm, to learn skills. We compare learned skills to existing skills in the CMDragons’ architecture using a physically realistic simulator. We then show how RL can be leveraged to learn simple skills that can be combined by humans into high level tactics that allow an agent to navigate to a ball, aim and shoot on a goal.