Superhuman AI for Generals.io Using Self-Play Reinforcement Learning

We present a superhuman AI agent for Generals.io, a real-time strategy game that combines long-horizon planning, imperfect information, and combinatorially large state and action spaces. Much prior work on large games relies on heavy machinery such as leagues of past opponents, reward shaping, and human data. We instead train a transformer policy with plain policy-gradient self-play on the sparse win/loss reward, and carefully ablate which ingredients carry the performance: an exponential moving average of the parameters is consistently the stronger deployment policy, and training only on the highest-advantage samples halves the cost of a training iteration. With seven days on four GPUs the agent reaches superhuman play: it ranked first in all ten weekly standings of a two-month deployment on the public 1v1 ladder, and it beat the two strongest humans 189-67 and 32-18. We also release a JAX-native simulator that reaches over 40 million frames per second on one GPU.