ml-agents and rl-llm-urban-simulations
ML-Agents provides the foundational reinforcement learning framework and simulation environment infrastructure that rl-llm-urban-simulations builds upon to create specialized multi-agent simulations with language model integration.
About ml-agents
Unity-Technologies/ml-agents
The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
This toolkit helps game developers and researchers create intelligent characters and systems within Unity games and simulations. You provide a Unity game environment, and the toolkit outputs trained AI agents that can control Non-Player Characters (NPCs), automate game testing, or evaluate design choices. Game developers and AI researchers are the primary users.
About rl-llm-urban-simulations
lukehollis/rl-llm-urban-simulations
Build RL+LLM multi-agent simulations in game engines for realistic human behaviors and studying cities
This system helps urban planners, transportation analysts, and logistics managers create highly realistic simulations of city-scale human behavior. It takes demographic data and environmental factors as input, then generates a diverse population of virtual agents who make complex decisions about transportation, interactions, and even disease spread within a game engine environment. The output is a dynamic, visual simulation that models how people move and behave in a city.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work