Kick-motion Training with DQN in AI Soccer Environment

TL;DR

This paper introduces a reinforcement learning approach using Deep Q-network with a relative coordinate system to efficiently train a robot for kick-motion in AI soccer, reducing the curse of dimensionality and improving training effectiveness.

Contribution

The paper proposes using a relative coordinate system for state representation in RL to mitigate the curse of dimensionality in robot kick-motion training.

Findings

RCS-based training reduces state space complexity.

DQN effectively learns kick-motion in AI soccer environment.

Training performance improves with RCS compared to ACS.

Abstract

This paper presents a technique to train a robot to perform kick-motion in AI soccer by using reinforcement learning (RL). In RL, an agent interacts with an environment and learns to choose an action in a state at each step. When training RL algorithms, a problem called the curse of dimensionality (COD) can occur if the dimension of the state is high and the number of training data is low. The COD often causes degraded performance of RL models. In the situation of the robot kicking the ball, as the ball approaches the robot, the robot chooses the action based on the information obtained from the soccer field. In order not to suffer COD, the training data, which are experiences in the case of RL, should be collected evenly from all areas of the soccer field over (theoretically infinite) time. In this paper, we attempt to use the relative coordinate system (RCS) as the state for training kick-motion of robot agent, instead of using the absolute coordinate system (ACS). Using the RCS eliminates the necessity for the agent to know all the (state) information of entire soccer field and reduces the dimension of the state that the agent needs to know to perform kick-motion, and consequently alleviates COD. The training based on the RCS is performed with the widely used Deep Q-network (DQN) and tested in the AI Soccer environment implemented with Webots simulation software.