A simulated fighter, coached like a real one.

The AI fighter's network had started trading old skills for new ones. To find out whether it was full, I grew it four ways without changing what it does and trained each beside an unchanged control. The bigger networks got 20–41 % closer to their teachers; in the fight tests, none was clearly better.

What goes on inside the AI fighter while it takes a shove, falls over and throws fourteen techniques: every joint's torque and every neuron of its second layer, shown together. The hips drive the punches, a press wakes over a hundred neurons before the body moves, and the network knows it will fall 1.9 s before it does.

What if the fighter were driven by muscles, not motors in its joints? It can learn the stance: in 2–3 times the training with simple joint muscles, 5–6 times with 142 muscles including real leg anatomy. It didn't learn to punch, and the reason is a muscle problem.

How a Muay Thai technique gets from my body into a simulated fighter: two-phone motion capture, a table of variations, a teacher, a student, and a human check at every step. Then one case in detail: a rear knee that scored well, recovered its balance, and was still wrong.

Instead of teaching one fighter every new technique in turn, I copied the latest model into one-technique teachers on rented GPUs, then distilled what they learnt back into a single student. Three techniques came back; some clones finished training and were still wrong.