Before diving into what we’ve built, here’s the baseline we started from:
- AGI won’t emerge from isolated algorithms. It will require a shared ecosystem where researchers can train, benchmark, and learn together in open environments.
- We believe RL is the most promising pathway toward general intelligence.
- Most RL researchers are still publishing results in isolation, on tasks that can’t easily be compared.
So, we built SAI, a RL competition platform designed to make RL progress more accessible, standardized, and measurable.
SAI is a platform where you can train, benchmark, and submit models to a global leaderboard. A proving ground for reproducible RL research:
- Competitions designed to surface real research challenges (generalization, transfer, and adaptation)
- Infrastructure for reproducible experiments and shared results
- Community through discussion forums, visible progress and collaboration
With SAI live, the next step is competition, and our second one launches October 6: the Booster Soccer Showdown, in partnership with Booster Robotics.
The challenge itself asks a core AGI question in miniature:
Can one agent generalize across different environments without per-task tuning?
Competitors will need to train a humanoid soccer agent to succeed at three related tasks - testing policies for adaptability, transfer, and generalization, the very qualities real-world intelligence requires.
If you’re into RL or just curious about ML, feel free to try out the platform. All feedback and ideas are welcome!
Platform: https://competesai.com/
Booster Soccer Showdown: https://competesai.com/competitions/cmp_xnSCxcJXQclQ