CROW: CROBOTS robotic combat for training World Model AIs (github.com)

CROW, an enhancement of the classic CROBOTS robotic combat game, has been introduced primarily for training world model AIs through realistic, physics-accurate battle simulations. The new system allows users to generate deterministic datasets of game states by executing various robot battle configurations, empowering developers to create adaptable AI agents. With functionalities like configurable battlefield dimensions and customizable snapshot grids, CROW produces structured data that capture detailed robot, missile, and battlefield states, all in a human-readable format that is readily tokenizable for machine learning applications. This advancement is significant for the AI/ML community as it provides a robust framework for developing, testing, and training AI algorithms in combat scenarios. By allowing AI agents to dynamically adjust their strategies based on battlefield conditions, CROW fosters heightened adaptability in robot behaviors. Key technical features include new functions for runtime battlefield configuration access, enabling robots to adapt their tactics without hardcoded limitations. The expanded sampling options further enhance the quality of training data, making CROW a vital tool for machine learning researchers focused on dynamic and interactive environments.