Starcraft

126 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 )

Libraries

Use these libraries to find Starcraft models and implementations
4 papers
10
3 papers
1,698
3 papers
34
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Most implemented papers

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

oxwhirl/wqmix NeurIPS 2020

We show in particular that this projection can fail to recover the optimal policy even with access to $Q^*$, which primarily stems from the equal weighting placed on each joint action.

Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning

vwxyzjn/gym-microrts 21 May 2021

In recent years, researchers have achieved great success in applying Deep Reinforcement Learning (DRL) algorithms to Real-time Strategy (RTS) games, creating strong autonomous agents that could defeat professional players in StarCraft~II.

TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game

Tencent/TStarBots 19 Sep 2018

Both TStarBot1 and TStarBot2 are able to defeat the built-in AI agents from level 1 to level 10 in a full game (1v1 Zerg-vs-Zerg game on the AbyssalReef map), noting that level 8, level 9, and level 10 are cheating agents with unfair advantages such as full vision on the whole map and resource harvest boosting.

Towards Accurate Generative Models of Video: A New Metric & Challenges

wilson1yan/VideoGPT 3 Dec 2018

To this extent we propose Fr\'{e}chet Video Distance (FVD), a new metric for generative models of video, and StarCraft 2 Videos (SCV), a benchmark of game play from custom starcraft 2 scenarios that challenge the current capabilities of generative models of video.

Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks

IC3Net/IC3Net ICLR 2019

Learning when to communicate and doing that effectively is essential in multi-agent tasks.

FACMAC: Factored Multi-Agent Centralised Policy Gradients

schroederdewitt/multiagent_mujoco NeurIPS 2021

We propose FACtored Multi-Agent Centralised policy gradients (FACMAC), a new method for cooperative multi-agent reinforcement learning in both discrete and continuous action spaces.

MazeBase: A Sandbox for Learning from Games

facebook/MazeBase 23 Nov 2015

This paper introduces MazeBase: an environment for simple 2D games, designed as a sandbox for machine learning approaches to reasoning and planning.

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.

Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games

isp1tze/MAProj 29 Mar 2017

Many artificial intelligence (AI) applications often require multiple intelligent agents to work in a collaborative effort.

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.