Starcraft

60 papers with code • 0 benchmarks • 6 datasets

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 )

Greatest papers with code

StarCraft II: A New Challenge for Reinforcement Learning

deepmind/pysc2 16 Aug 2017

Finally, we present initial baseline results for canonical deep reinforcement learning agents applied to the StarCraft II domain.

Starcraft Starcraft II

ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games

facebookresearch/ELF NeurIPS 2017

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.

Atari Games Starcraft

TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games

TorchCraft/TorchCraft 1 Nov 2016

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.

Starcraft

Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

oxwhirl/pymarl 19 Mar 2020

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.

SMAC Starcraft

The StarCraft Multi-Agent Challenge

oxwhirl/pymarl 11 Feb 2019

In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap.

SMAC Starcraft +1

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

oxwhirl/pymarl ICML 2018

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.

Multi-agent Reinforcement Learning Starcraft +1

A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning

TorchCraft/TorchCraftAI NeurIPS 2019

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 +1

Growing Action Spaces

TorchCraft/TorchCraftAI ICML 2020

In complex tasks, such as those with large combinatorial action spaces, random exploration may be too inefficient to achieve meaningful learning progress.

Starcraft

STARDATA: A StarCraft AI Research Dataset

TorchCraft/StarData 7 Aug 2017

We provide full game state data along with the original replays that can be viewed in StarCraft.

Imitation Learning Starcraft

A Dataset for StarCraft AI \& an Example of Armies Clustering

TorchCraft/StarData 19 Nov 2012

We evaluated this clustering method by predicting the outcomes of battles based on armies compositions' mixtures components

Starcraft