Starcraft
Starcraft I is a RTS game; the task is to train an agent to play the game.
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StarCraft II: A New Challenge for Reinforcement Learning
Finally, we present initial baseline results for canonical deep reinforcement learning agents applied to the StarCraft II domain.
ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games
NeurIPS 2017
• facebookresearch/ELF
• 
In addition, our platform is flexible in terms of environment-agent communication topologies, choices of RL methods, changes in game parameters, and can host existing C/C++-based game environments like Arcade Learning Environment.
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.
STARDATA: A StarCraft AI Research Dataset
We provide full game state data along with the original replays that can be viewed in StarCraft.
A Dataset for StarCraft AI \& an Example of Armies Clustering
We evaluated this clustering method by predicting the outcomes of battles based on armies compositions' mixtures components
MazeBase: A Sandbox for Learning from Games
This paper introduces MazeBase: an environment for simple 2D games, designed as a sandbox for machine learning approaches to reasoning and planning.
Multi-platform Version of StarCraft: Brood War in a Docker Container: Technical Report
• Games-and-Simulations/sc-docker
We present a dockerized version of a real-time strategy game StarCraft: Brood War, commonly used as a domain for AI research, with a pre-installed collection of AI developement tools supporting all the major types of StarCraft bots.
The StarCraft Multi-Agent Challenge
• oxwhirl/pymarl
•
In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap.
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
At the same time, it is often possible to train the agents in a centralised fashion in a simulated or laboratory setting, where global state information is available and communication constraints are lifted.
MSC: A Dataset for Macro-Management in StarCraft II
We also split MSC into training, validation and test set for the convenience of evaluation and comparison.