Speeding Up the Learning in A Robot Simulator

Abstract

Q-learning is a one of the well-known Reinforcement Learning algorithms that has been widely used in various problems. The main contribution of this work is how to speed up the learning in a single agent environment (e.g. the robot). In this work, an attempt to optimize the traditional Q-learning algorithm has been done via using the Repeated Update Q-learning (RUQL) algorithm (the recent state-of-the-art) in a robot simulator. The robot simulator should learn how to move from one state into another in order to reach the end of screen as faster as possible. An experiment has been conducted in order test the effectiveness of the RUQL algorithm versus the traditional Q-learning algorithm by comparing both algorithms through using similar parameters' values for several trials. Experiment results revealed that the RUQL algorithm has outperforms the traditional Q-learning algorithm in all the trials.