legalaspro/unity_multiagent_rl
Multi-agent reinforcement learning framework for Unity environments. Implements MAPPO, MASAC, MATD3, and MADDPG with comprehensive evaluation tools. Features sample-efficient training, competitive analysis, and pre-trained models achieving great performance in Tennis and Soccer environments.
This framework helps AI researchers and game developers train and evaluate groups of AI agents in simulated Unity environments. You input a Unity environment (like a tennis or soccer game) and configuration settings for the AI, and it outputs trained AI models capable of complex cooperative or competitive behaviors. It's designed for those working with multi-agent reinforcement learning in gaming or simulation.
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Use this if you need to develop and test advanced AI behaviors for multiple agents interacting in Unity-based simulations or games.
Not ideal if you are looking to develop single-agent AI or if your simulation environment is not built with Unity.
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Language
Python
License
MIT
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Last pushed
May 30, 2025
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