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Found 6 papers, 5 papers with code
Terrain Analysis in StarCraft 1 and 2 as Combinatorial Optimization
The goal of terrain analysis is to gather and process data about the map topology and properties to have a qualitative spatial representation.
microPhantom: Playing microRTS under uncertainty and chaos
microPhantom is based on our previous bot POAdaptive which won the partially observable track of the 2018 and 2019 microRTS AI competitions.
Learning Interpretable Error Functions for Combinatorial Optimization Problem Modeling
In Constraint Programming, constraints are usually represented as predicates allowing or forbidding combinations of values.
Comparing two deep learning sequence-based models for protein-protein interaction prediction
However, it is easy to be get a deep learning model that seems to have good results but is in fact either overfitting the training data or the validation data.
Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy games
However, few Constraint Programming formalisms can deal with both optimization and uncertainty at the same time, and none of them are convenient to model problems we tackle in this paper.
TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games
We present TorchCraft, a library that enables deep learning research on Real-Time Strategy (RTS) games such as StarCraft: Brood War, by making it easier to control these games from a machine learning framework, here Torch.
