MAPF_G2RL is an open-source research framework that bridges graph theory and deep reinforcement learning to tackle the multi-agent path finding (MAPF) problem. It encodes nodes and edges into vector representations, defines spatial and collision-aware reward functions, and supports various RL algorithms such as DQN, PPO, and A2C. The framework automates scenario creation by generating random graphs or importing real-world maps, and orchestrates training loops that optimize policies for multiple agents simultaneously. After learning, agents are evaluated in simulated environments to measure path optimality, makespan, and success rates. Its modular design allows researchers to extend core components, integrate new MARL techniques, and benchmark against classical solvers.

Melies is an advanced AI filmmaking software designed to help users transform their creative ideas into professional-quality movies. It offers unique tools for generating story ideas, crafting character bibles, and writing compelling screenplays. Additionally, Melies provides powerful video generation and editing capabilities, turning your scripts into animated storyboards and completed films. By integrating numerous generative AI tools, Melies supports various stages of the filmmaking process, making it easier than ever for independent filmmakers to produce high-quality content without the backing of large studios.

EthicalEvalMAS provides a modular environment to assess multi-agent systems across key ethical dimensions such as justice, autonomy, privacy, transparency, and beneficence. Users can generate custom scenarios or use built-in templates, define bespoke metrics, execute automated evaluation scripts, and visualize outcomes through built-in reporting tools. Its extensible architecture supports integration with existing MAS platforms and facilitates reproducible ethical benchmarking across different agent behaviors.