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.

Enso is a browser-based platform that lets users create custom AI agents through a visual flow-based builder. Users drag and drop modular code and AI components, configure API integrations, embed chat interfaces, and preview interactive workflows in real time. Once designed, agents can be tested instantly and deployed with one click to the cloud or exported as containers. Enso simplifies complex automation tasks by combining no-code simplicity with full code extensibility, enabling rapid development of intelligent assistants and data-driven workflows.