Multi-Agent Miners offers a grid-world environment where multiple autonomous miner agents navigate, dig, and collect resources while interacting with each other. It supports configurable map sizes, agent counts, and reward structures, allowing users to create competitive or cooperative scenarios. The framework integrates with popular RL libraries via PettingZoo, providing standardized APIs for reset, step, and render functions. Visualization modes and logging support help analyze behaviors and outcomes, making it ideal for research, education, and algorithm benchmarking in multi-agent reinforcement learning.

The AI Agent Network Protocol is designed to foster communication and interaction among different AI agents, allowing them to exchange data, execute tasks collaboratively, and adapt to user requirements in real-time. It enhances interoperability and efficiency, promoting dynamic task sharing and resource optimization across diverse applications in sectors such as automation, customer support, and data analysis.