Exploring Smartphone-Based Reinforcement Learning Control for Educational Robotics: Implementation on OpenBot

Abstract

This research explores the feasibility of implementing Reinforcement Learning (RL) algorithms entirely on a smartphone to control an educational robotic platform, OpenBot. This study aims to determine if RL can be executed on Android smartphones without simulated environments and whether it would be accessible for students and enthusiasts as a practical RL project. Initially, Deep Q-Learning (DQL) and Policy Gradient (PG) algorithms were tested on standard RL scenarios, Cartpole and Pong. This allowed to gain insights on both algorithms and what to expect in a successful RL training. The policy gradient algorithm was then implemented entirely on the smartphone controlling OpenBot to drive across a track for 15 seconds. In general, after approximately 400 episodes of training using policy gradient, the agent was able to successfully navigate the track for the aimed 15 seconds in half of its attempts. Despite the encouraging results of the study, some technical challenges remain open, such as, exploding gradients, the randomness of weight initialization, and engineering challenges such as high battery consumption.