Games To General Intelligence

Games are ideal for reinforcement learning because they offer well-defined rules, explicit reward functions, and controlled state/action spaces, enabling unambiguous evaluation of agent behaviour.

They can also be designed to demand exploration in large or partially observable state spaces, long-horizon planning through delayed rewards, and coordination via multi-agent dynamics—making them both tractable for training and rich enough to develop the skills frontier models still lack.