This is a really interesting direction. RTS games are a much better testbed for agent capability than most static benchmarks because they combine partial observability, long-term planning, resource management, and real-time adaptation.
It reminds me a bit of OpenAI Five — not just because it played a complex game, but because the real value wasn’t “AI plays Dota,” it was observing how coordination, strategy formation, and adaptation emerged under competitive pressure. A controlled RTS environment like this feels like a lightweight, reproducible version of that idea.
What I especially like here is that it lowers the barrier for experimentation. If researchers and hobbyists can plug different models into the same competitive sandbox, we might start seeing meaningful AI-vs-AI evaluations beyond static leaderboards. Competitive dynamics often expose weaknesses much faster than isolated benchmarks do.
Curious whether you’re planning to support self-play training loops or if the focus is primarily on inference-time agents?
> partial observability, long-term planning, resource management, and real-time adaptation
Note, this project doesn't have that best I can tell? Its two static AI scripts having a go. LLMs generate the scripts and they are aware of past "results", but I'm not sure what that means.
Very interested in self-play training loops, but I do like codegen as an abstraction layer. I am planning to make it available as an RL environment at some point
It reminds me a bit of OpenAI Five — not just because it played a complex game, but because the real value wasn’t “AI plays Dota,” it was observing how coordination, strategy formation, and adaptation emerged under competitive pressure. A controlled RTS environment like this feels like a lightweight, reproducible version of that idea.
What I especially like here is that it lowers the barrier for experimentation. If researchers and hobbyists can plug different models into the same competitive sandbox, we might start seeing meaningful AI-vs-AI evaluations beyond static leaderboards. Competitive dynamics often expose weaknesses much faster than isolated benchmarks do.
Curious whether you’re planning to support self-play training loops or if the focus is primarily on inference-time agents?