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This paper presents a technique to train a robot to perform kick-motion in AIsoccer by using reinforcement learning (RL). In RL, an agent interacts with anenvironment and learns to choose an action in a state at each step. Whentraining RL algorithms, a problem called the curse of dimensionality (COD) canoccur if the dimension of the state is high and the number of training data islow. The COD often causes degraded performance of RL models. In the situationof the robot kicking the ball, as the ball approaches the robot, the robotchooses the action based on the information obtained from the soccer field. Inorder not to suffer COD, the training data, which are experiences in the caseof RL, should be collected evenly from all areas of the soccer field over(theoretically infinite) time. In this paper, we attempt to use the relativecoordinate system (RCS) as the state for training kick-motion of robot agent,instead of using the absolute coordinate system (ACS). Using the RCS eliminatesthe necessity for the agent to know all the (state) information of entiresoccer field and reduces the dimension of the state that the agent needs toknow to perform kick-motion, and consequently alleviates COD. The trainingbased on the RCS is performed with the widely used Deep Q-network (DQN) andtested in the AI Soccer environment implemented with Webots simulationsoftware.

Kick-motion Training with DQN in AI Soccer Environment