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 Berkeley Pacman Projects repository offers a modular Python codebase where users build and test AI agents in a Pacman maze. It guides learners through uninformed and informed search (DFS, BFS, A*), adversarial multi-agent search (minimax, alpha-beta pruning), and reinforcement learning (Q-learning with feature extraction). Integrated graphical interfaces visualize agent behavior in real time, while built-in test cases and an autograder verify correctness. By iterating on algorithm implementations, users gain practical experience in state space exploration, heuristic design, adversarial reasoning, and reward-based learning within a unified game framework.

Edukade is an innovative platform designed to revolutionize the way educational content is created and delivered. By leveraging manual drag-and-drop tools or advanced AI technology, users can create engaging and interactive learning games. This empowers educators to save time while significantly enhancing student engagement and academic outcomes. The platform aims to make learning enjoyable and effective, providing a versatile toolset for teachers, parents, and educational institutions to facilitate a more dynamic and interactive learning experience.