Pits and Orbs is an open-source reinforcement learning environment implemented in Python, offering a turn-based multi-agent grid-world where agents pursue objectives and face environmental hazards. Each agent must navigate a customizable grid, avoid randomly placed pits that penalize or terminate episodes, and collect orbs for positive rewards. The environment supports both competitive and cooperative modes, enabling researchers to explore varied learning scenarios. Its simple API integrates seamlessly with popular RL libraries like Stable Baselines or RLlib. Key features include adjustable grid dimensions, dynamic pit and orb distributions, configurable reward structures, and optional logging for training analysis.

PracticeRun is an innovative platform designed to optimize practice schedules, track performance metrics, and aid in the development of sports teams as well as individual athletes. It offers a robust set of tools for coaches and players to plan, execute, and review practice sessions efficiently. The platform provides detailed analytics to help users understand their progress and identify areas for improvement. With PracticeRun, managing practice routines becomes more systematic and data-driven, paving the way for better performance and goal achievement.