MARBLER: An Open Platform for Standardized Evaluation of Multi-Robot Reinforcement Learning Algorithms

• Published in: arXiv.org 2023-10
• Main Authors: Torbati, Reza, Lohiya, Shubham, Singh, Shivika, Nigam, Meher Shashwat, Ravichandar, Harish
• Format: Article
• Language: English
• Subjects: Algorithms; Controllability; Deep learning; Machine learning; Multiagent systems; Multiple robots; Obstacle avoidance; Performance evaluation; Rapid prototyping; Robot dynamics; Robots
• ISSN: 2331-8422

Multi-Agent Reinforcement Learning (MARL) has enjoyed significant recent progress thanks, in part, to the integration of deep learning techniques for modeling interactions in complex environments. This is naturally starting to benefit multi-robot systems (MRS) in the form of multi-robot RL (MRRL). However, existing infrastructure to train and evaluate policies predominantly focus on the challenges of coordinating virtual agents, and ignore characteristics important to robotic systems. Few platforms support realistic robot dynamics, and fewer still can evaluate Sim2Real performance of learned behavior. To address these issues, we contribute MARBLER: Multi-Agent RL Benchmark and Learning Environment for the Robotarium. MARBLER offers a robust and comprehensive evaluation platform for MRRL by marrying Georgia Tech's Robotarium (which enables rapid deployment on physical MRS) and OpenAI's Gym interface (which facilitates standardized use of modern learning algorithms). MARBLER offers a highly controllable environment with realistic dynamics, including barrier certificate-based obstacle avoidance. It allows anyone across the world to train and deploy MRRL algorithms on a physical testbed with reproducibility. Further, we introduce five novel scenarios inspired by common challenges in MRS and provide support for new custom scenarios. Finally, we use MARBLER to evaluate popular MARL algorithms and provide insights into their suitability for MRRL. In summary, MARBLER can be a valuable tool to the MRS research community by facilitating comprehensive and standardized evaluation of learning algorithms on realistic simulations and physical hardware. Links to our open-source framework and videos of real-world experiments can be found at https://shubhlohiya.github.io/MARBLER/.