marlbenchmark/on-policy
This is the official implementation of Multi-Agent PPO (MAPPO).
This project helps researchers and practitioners in multi-agent systems develop and evaluate advanced artificial intelligence for cooperative scenarios. It takes multi-agent environment data from simulations like StarCraft II, Hanabi, or Google Research Football and outputs optimized policies for agents to collaborate effectively. Anyone working on AI for teams, autonomous systems, or complex game environments would find this useful.
1,914 stars. No commits in the last 6 months.
Use this if you are developing or studying AI agents that need to learn cooperative behaviors in multi-agent simulation environments.
Not ideal if you are looking for a simple, out-of-the-box solution for single-agent tasks or real-world robotics deployment without simulation.
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Language
Python
License
MIT
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Last pushed
Jul 18, 2024
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