Teaching Continuous Action Space Reinforcement Learning with a Mobile Robot - Martins, Thiago; Driemeier, Larissa (Anais do Simpósio Brasileiro de Robótica e Simpósio Latino Americano de Robótica (SBR/LARS), 2025)
This paper presents a teaching framework that integrates continuous action space reinforcement learning with a mobile robotics platform. Reinforcement learning (RL) enables agents to learn optimal decision-making policies through interaction with their environment, making it a natural fit for robotics applications. The study examines the challenges and solutions for applying RL algorithms, specifically SARSA and Actor-Critic methods, to continuous state and action spaces. These methods are implemented and compared on a differential-drive robot tasked with maintaining a specific orientation and position. A virtual model is utilized to facilitate safe and efficient learning before deploying the algorithms to a physical robot. This study provides practical insights into teaching reinforcement learning concepts and leveraging them for robotics applications.