Development of a Reinforcement Learning Concept for Robot Navigation in 2D Environments for Even Terrain World

This paper offers a thorough examination of their application of Reinforcement Learnings (RL) in robotic systems, particularly focusing on navigation tasks in complex environments. The primary goal is to enhance the adaptability, efficiency, and generalization capabilities of robots. The study provides an overview of RL concepts, emphasizing their relevance to robot navigation, and evaluates various RL algorithms, including Deep Reinforcement Learning (DRL). It analyzes considerations such as state representation, action space, and reward shaping and explores the integration of sensor data, mapping techniques, and Simultaneous Localizations and Mappings (SLAM) with RL frameworks to improve the robot’s perception. The paper discusses the adaptation of RL to real-world challenges, such as dynamic obstacles and unknown terrains, presenting case studies and experiments to illustrate practical applications. The conclusion includes a discussion on current challenges, future research directions, and potential advancements, aiming to serves as a comprehensive resource for the researcher and practitioner interested in utilizing RL to enhance autonomous robot’s navigations in complexes and dynamical environments.