WorldQL is an advanced game engine designed to accelerate the creation and deployment of reinforcement learning (RL) environments. By leveraging web technologies, it enables developers and researchers to build, test, and ship RL environments in hours instead of weeks. WorldQL supports real-time collaboration, allowing multiple users and agents to edit the same environment simultaneously, akin to the collaborative experience of Google Docs. Its compatibility across various platforms, including macOS, Linux, and Windows via WSL, ensures flexibility and ease of deployment for diverse applications. Key Features and Functionality: - Real-time Collaboration: Facilitates simultaneous editing by multiple users and agents, enhancing teamwork and efficiency. - Maximum Compatibility and Flexibility: Built on web technologies, ensuring easy deployment and adaptability across different applications. - Fast Iteration: Enables rapid building, testing, and deployment of environments, significantly reducing development time. - Defeat Reward Hacking: Provides tools to analyze session replays, helping identify and address issues early in training runs. - Comprehensive Version Control: Offers visual history tracking, easy collaboration without additional software, and visual merge conflict resolution within the browser. - Versatile Deployment: Supports exporting environments to OpenEnv-compatible Docker containers, facilitating seamless integration into various workflows. Primary Value and Problem Solved: WorldQL addresses the challenges of time-consuming and complex RL environment development by providing a collaborative, flexible, and efficient platform. It empowers developers and researchers to create and deploy sophisticated RL environments swiftly, enhancing productivity and innovation in AI training and evaluation.