Tips for an AI for a 2D racing game

Question

I have a school project to build an AI for a 2D racing game in which it will compete with several other AIs.

We are given a black and white bitmap image of the racing track, we are allowed to choose basic stats for our car (handling, acceleration, max speed and brakes) after we receive the map. The AI connects to the game's server and gives to it several times a second numbers for the current acceleration and steering. The language I chose is C++, by the way. The question is

1. What is the best strategy or algorithm (since I want to try and win)? I currently have in mind some ideas found on the net and one or two of my own, but I would like before I start to code that my perspective is one of the best.
2. What good books are there on that matter?
3. What sites should I refer to?

Answer 1

There's no "right answer" for this problem - it's pretty open-ended and many different options might work out.

You may want to look into reinforcement learning as a way of trying to get the AI to best determine how to control the car once it's picked the different control statistics. Reinforcement learning models can train the computer to try to work toward a good system for making particular maneuvers in terms of the underlying control system.

To determine what controls you'll want to use, you could use some flavor of reinforcement learning, or you may want to investigate supervised learning algorithms that can play around with different combinations of controls and see how good of a "fit" they give for the particular map. For example, you might break the map apart into small blocks, then try seeing what controls do well in the greatest number of blocks.

In terms of plotting out the path you'll want to take, A* is a well-known algorithm for finding shortest paths. In your case, I'm not sure how useful it will be, but it's the textbook informed search algorithm.

For avoiding opponent racers and trying to drive them into trickier situations, you may need to develop some sort of opponent modeling system. Universal portfolios are one way to do this, though I'm not sure how useful they'll be in this instance. One option might be to develop a potential field around the track and opponent cars to help your car try to avoid obstacles; this may actually be a better choice than A* for pathfinding. If you're interested in tactical maneuvers, a straightforward minimax search may be a good way to avoid getting trapped or to find ways to trap opponents.

I am no AI expert, but I think the above links might be a good starting point. Best of luck with the competition!

Answer 2

What good books are there on that matter?

The best book I have read on this subject is "Programming Game AI by Example" by Mat Buckland. It has chapters on both path planning and steering behaviors, and much more (state machines, graph theory, the list goes on).

Answer 3

All the solutions above are good, and people have gone to great length to test them out. Look up "Togelius and Lucas" or "Loiacono and Lanzi". They have tries things like neuroevolution, imitation (done via reinforcement learning), force fields, etc. From my point of view the best way to go is center line. That will take an hour to implement. In contrast, neuroevolution (for example) is neither easy nor fast. I did my dissertation on that and it can easily take several months full time, if you have the right hardware.