StarCraft Learning
Algorithms
By Logan Yarnell, Steven Raines, and Dean Antel
Abstract

The opening or initial build order during a StarCraft match is an integral part
of a player’s strategy. If an opening is naively chosen it makes a player
vulnerable to tactics that can end the game very quickly. We examine three
algorithms for choosing a “good” opener: Bayesian network, a genetic
algorithm, and a pathfinding algorithm.
About StarCraft

Real-time strategy game

Similar to a complex digital version of chess but in real-time

Expert level gameplay is very complex, often requiring over 300 actions per
minute.

Considered a national sport in South Korea
About StarCraft

Playing Starcraft requires simultaneously managing several distinct
competencies:

Strategy

Production

Economy

Recon

Tactics
Basic Actions

Build buildings

Build units

Attack the enemy

Gather resources/manage economy
Genetic Algorithm

Modifies some set of “solutions” to a problem in a way similar to natural
selection

Has mutation, crossover, and elitism functions

Solution = a build order

Each build order is given a fitness value determined by how successful it is

The number of mutations the build has gone through is also recorded

If a build order has gone through 10 mutations and it’s fitness < 5, elitism
removes that build order
Genetic Algorithm

Most important part of genetic algorithm = mutate function

Sometimes the only part implemented if computational resources are limited

Fitness value determination is also important

Considered factors in determining a build’s fitness: time taken, amount of
resources used, whether the match was won or lost, number of
units/structures that survived…

In the end only win/loss rate was chosen to affect fitness
Genetic Algorithm

Mutate function randomly chooses to add a new unit, reorder the build, or
remove a unit

isBuildValid method checks to make sure the mutate function doesn’t produce
a useless build order by placing a unit before it’s prerequisite(s)

The newly mutated build is then written to a .txt file with NumOfMutations
incremented

A match is run with that build, if it wins the fitness value is incremented, if it
looses the fitness is not altered

There is an element of randomness, but complexity is more or less constant
Genetic Algorithm
Genetic Algorithm
Bayesian Network

Use knowledge from earlier games to predict what type of units the opponent
is likely to have.

Requires mining a large amount of data from replays

Scouting is essential to make this work effectively

Difficult due to incomplete information available during matches
Pathfinding

From what is seen, create a shortest possible build. Relies on scouting.

Dijkstra’s algorithm O(n) complexity.

O(|V|^2)

O(|E|+|V|log|V|)

Intersection O(n^2)
Pathfinding (Continued)
Conclusions

Understanding the complexity of the environment is a key element to
creating a learning algorithm for it.

Complementary not competitive.
Why 380?

There are many ways to implement a StarCraft bot that include various
algorithms for the different aspects of gameplay:

Finite state machines

Scripting

Dynamic scripting

Probabilistic inference

Influence maps

Neural networks

Swarm intelligence

Potential fields

Genetic programming
Future Work

Enhance unit combat

Increase the amount of units considered in build orders

Optimize building efficiency to allow multiple buildings to be constructed at
the same time.

Add dynamic scouting
Questions

What is the computational complexity (O(n)) of Dijkstra’s algorithm without
optimization?

Is this a constraint satisfaction problem, or an optimization problem?

What probabilistic relationship is there between nodes in a Bayesian network
that represents a StarCraft tech tree?
Answers

N^2

Optimization

Conditional dependence
Sources

http://www.sscaitournament.com/

http://web.engr.oregonstate.edu/~tgd/publications/aiide2011-starcraftmodel.pdf

http://arxiv.org/pdf/1111.3735.pdf

http://plankter.se/wpcontent/uploads/2014/12/making_and_acting_on_predictions_on_scbw_2014.
pdf