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Chrono-Gymnasium: An Open-Source, Gymnasium-Compatible Distributed Simulation Framework

2026-05-14 · Bocheng Zou, Harry Zhang, Khailanii Slaton, Jingquan Wang, Derrick Ruan, Huzaifa Mustafa Unjhawala, Radu Serban, Dan Negrut arxiv

High-fidelity physics simulation is essential for closing the sim-to-real gap in robotics and complex mechanical systems. However, the computational overhead of high-fidelity engines often limits their use in data-intensive tasks like Reinforcement Learning (RL) and global optimization. We introduce Chrono-Gymnasium, a distributed computing framework that scales the high-fidelity multi-body dynamics of Project Chrono across large-scale computing clusters. Built upon the Ray framework, Chrono-Gymnasium provides a standardized Gymnasium interface, enabling seamless integration with modern machine learning libraries while providing built-in synchronization and messaging primitives for distributed execution. We demonstrate the framework's capabilities through two distinct case studies: (1) the training of an RL agent for autonomous robotic navigation in complex terrains, and (2) the Bayesian Optimization of a planetary lander's design parameters to ensure landing stability. Our results show that Chrono-Gymnasium reduces wall-clock time for high-fidelity simulations without sacrificing physical accuracy, offering a scalable path for the design and control of complex robotic systems.

📄 PDF Abstract BibTeX arXiv:2605.14911

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Reinforcement Learning

Gymnasium: A Standard Interface for Reinforcement Learning Environments

2024-07-24 · Mark Towers, Ariel Kwiatkowski, Jordan Terry, John U. Balis 외

Reinforcement Learning (RL) is a continuously growing field that has the potential to revolutionize many areas of artificial intelligence. However, despite its promise, RL research is often hindered by the lack of standa…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning

2023-09-26 · Conference on Neural Information Processing Systems Datasets and Benchmarks Track 2023 9 · Florian Felten, Lucas N. Alegre, Ann Nowé, Ana L. C. Bazzan 외

Multi-objective reinforcement learning algorithms (MORL) extend standard reinforcement learning (RL) to scenarios where agents must optimize multiple---potentially conflicting---objectives, each represented by a distinct…

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JaxARC: A High-Performance JAX-based Environment for Abstraction and Reasoning Research

2026-01-24 · Aadam, Monu Verma, Mohamed Abdel-Mottaleb arxiv

The Abstraction and Reasoning Corpus (ARC) tests AI systems' ability to perform human-like inductive reasoning from a few demonstration pairs. Existing Gymnasium-based RL environments severely limit experimental scale du…

CoGrid & the Multi-User Gymnasium: A Framework for Multi-Agent Experimentation

2026-04-16 · Chase McDonald, Cleotilde Gonzalez arxiv

The increasing integration of artificial intelligence (AI) in everyday life brings with it new challenges and questions for regarding how humans interact with autonomous agents. Multi-agent experiments, where humans and …

Decision Making

Towards Affordable Energy: A Gymnasium Environment for Electric Utility Demand-Response Programs

2026-05-12 · Jose E. Aguilar Escamilla, Lingdong Zhou, Xiangqi Zhu, Huazheng Wang arxiv

Extreme weather and volatile wholesale electricity markets expose residential consumers to catastrophic financial risks, yet demand response at the distribution level remains an underutilized tool for grid flexibility an…