Starcraft AI blog

I compare 3 idealized attitudes a game programmer might take. Real people aren’t so simple, of course, but it’s a way to think about attitude. In reality, you can pick your own goals, or you can not even think about it and just do what comes naturally, whatever. The only prescription here is if your goal is this, then these points may help you approach it.

the winning attitude

Trying to come out on top.

I think that working on a game program is a lot like working on an industrial process or business procedure: The principles of continuous improvement apply.

• The goal is to reduce mistakes and inefficiencies.
• To win, reduce the biggest play errors.
• Thou shalt not suffer a bug to live. Even a minor bug detracts from your ability to understand what’s happening.
• Code for clarity and reliability.
• Test carefully to find bugs and understand what works and how.
• When you have a choice, go after the big win first. Don’t go astray solving little problems when you can solve a big one.
• Constantly try new ideas.
• Objective data tells you which ideas live or die. Your intuition may often be wrong.
• Never stop! There is no “good enough,” at most “good enough for today.”
• Dig in and analyze details. The deeper and wider your understanding, the better your new ideas will be.

See how chess programmer Ed Schröder does it. Chess programs are mature, of course, and improve in much smaller steps than Starcraft bots, but there are similarities.

“There will be errors in the program—hundreds of them—and it is only through extensive testing that the errors can be found and corrected.” — Jonathan Schaeffer, One Jump Ahead

the academic attitude

Trying to understand.

Typical long-term goals are to find a powerful technique that works to solve many different problems, or to understand theoretically when and why a given technique works. In practice, academics usually have to settle for step-by-step progress like everybody else. Typical methods include simplifying the problem or tackling one aspect at a time, to make it more tractable. An academic will not consider the work done when the program is proven to work, but only when the theory behind the program is proven to work—I think that that theoretical inclination is the most important difference between the academic attitude and the winning attitude: Theoretical versus practical.

The academic attitude is responsible for a lot of great AI ideas, from alpha-beta to deep learning. I think that the winning attitude is responsible for the lion’s share of the refinements and practical tricks in putting the great ideas to use. The two attitudes don’t exclude each other, after all.

The paper “Evaluating Real-Time Strategy Game States Using Convolutional Neural Networks” by Marius Stanescu, Nicolas A. Barriga, Andy Hess and Michael Buro in the CIG 2016 proceedings is a good example of the academic attitude. The authors want to apply deep learning with convolutional neural networks, as in AlphaGo, to RTS games. It’s a big problem that can’t be solved in one short paper, only started. And they don’t start with a full-size game like Starcraft, they start with a greatly simplified game as a testbed to begin to get some insight. The key conclusion: “several orders of magnitude slower than the other evaluation functions, but the accuracy gain far outweighs the speed disadvantage.”

the hobbyist attitude

Having fun.

This is more freeform. No bullet list! Do whatever’s interesting, try crazy experiments because they might turn out entertaining. Or repeat other people’s successes, if you think that’s fun. Some people do.

Tscmoo’s terran nuke and protoss dark archon strategies are the hobbyist attitude at work.