Starcraft
Starcraft I is a RTS game; the task is to train an agent to play the game.
( Image credit: Macro Action Selection with Deep Reinforcement Learning in StarCraft )
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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.
SOTA for Starcraft II on MoveToBeacon
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.
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning
While centralized reinforcement learning methods can optimally solve small MAC instances, they do not scale to large problems and they fail to generalize to scenarios different from those seen during training.
MULTI-AGENT REINFORCEMENT LEARNING STARCRAFT STRUCTURED PREDICTION
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
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
• oxwhirl/pymarl
•
At the same time, it is often possible to train the agents in a centralised fashion where global state information is available and communication constraints are lifted.
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.
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.