HexHoot is an open-source project designed to create a communication platform that respects user privacy and data ownership. By eliminating the need for centralized servers, it ensures that all data is stored locally on users' devices. HexHoot uses advanced zero-knowledge-proof strategies to authenticate users without compromising security. This approach makes it ideal for secure, transparent, and decentralized communication, free from the risks of traditional P2P software dependencies.

This open-source project delivers a complete multi-agent reinforcement learning framework built on PyTorch and Unity ML-Agents. It offers decentralized DDPG algorithms, environment wrappers, and training scripts. Users can configure agent policies, critic networks, replay buffers, and parallel training workers. Logging hooks allow TensorBoard monitoring, while modular code supports custom reward functions and environment parameters. The repository includes sample Unity scenes demonstrating collaborative navigation tasks, making it ideal for extending and benchmarking multi-agent scenarios in simulation.