Choosing the Arena: A Systematic Review of Simulators for Deep Reinforcement Learning in Mobile Robot Navigation

Abstract

This study presents a formal Systematic Literature Review (SLR) to address a critical methodological question in robotics research: “Which simulator is most suitable for a given Deep Reinforcement Learning (DRL) algorithm and mobile robot navigation task?” The choice of a simulation environment profoundly impacts policy robustness, data efficiency, and simto-real transfer, yet the community has lacked an evidence-based guide for this decision. Following PRISMA guidelines, we methodically searched and analyzed 87 peer-reviewed studies published between January 2020 and June 2025 to map the contemporary research landscape. Our synthesis introduces a novel, theory-informed taxonomy that classifies simulators into three archetypes based on their empirical use. Archetype I, ROS-centric standards (e.g., Gazebo), are chosen for algorithmic novelty with low-dimensional sensor inputs. Archetype II, versatile platforms (e.g., CoppeliaSim), are favored for rapid prototyping. Archetype III, GPU-native engines (e.g., NVIDIA Isaac Sim), have emerged for large-scale, perception-heavy challenges, leveraging photorealism and parallelization to mitigate the perception gap and enable zero-shot transfer. This review reveals a paradigm shift towards data-driven methodologies and culminates in a prescriptive decision-making framework, transforming simulator selection from an incidental detail into a strategic choice.