Home Research Papers Datasets State of the Art Pricing

Discover, visualize, and connect AI research papers. Explore the latest trends and insights in artificial intelligence research.

Product

Home
Research Papers
About

Support

Contact
Terms of Service
Privacy Policy

© 2026 Papersgraph. All rights reserved.

video-games

Real-Time Strategy Games

3260 papers • 126 benchmarks • 313 datasets

Real-Time Strategy (RTS) tasks involve training an agent to play video games with continuous gameplay and high-level macro-strategic goals such as map control, economic superiority and more. ( Image credit: Multi-platform Version of StarCraft: Brood War in a Docker Container )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in video-games

Trend

Dataset

Best Model

Actions

No benchmarks available.

Libraries

i

Use these libraries to find video-games models and implementations

Datasets

Mario AI

StarData

eSports Sensors Dataset

eSports Sensors Dataset

MSC

Subtasks

Starcraft Starcraft II

Most implemented papers

StarCraft II: A New Challenge for Reinforcement Learning

T. Ewalds, O. Vinyals, K. Simonyan, Julian Schrittwieser, Stig Petersen, David Silver, T. Lillicrap, H. V. Hasselt, T. Schaul, Petko Georgiev, John Quan, J. Agapiou, Sergey Bartunov, A. Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, Stephen Gaffney, Kevin Calderone, Paul Keet, Anthony Brunasso, David Lawrence, Anders Ekermo, J. Repp, Rodney Tsing•Tue Aug 15 2017

This paper introduces SC2LE (StarCraft II Learning Environment), a reinforcement learning environment based on the StarCraft II game that offers a new and challenging environment for exploring deep reinforcement learning algorithms and architectures and gives initial baseline results for neural networks trained from this data to predict game outcomes and player actions.

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

935 0

The StarCraft Multi-Agent Challenge

Tabish Rashid, Mikayel Samvelyan, C. S. D. Witt, Gregory Farquhar, Jakob N. Foerster, Shimon Whiteson, Philip H. S. Torr, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung•Sun Feb 10 2019

The StarCraft Multi-Agent Challenge (SMAC), based on the popular real-time strategy game StarCraft II, is proposed as a benchmark problem and an open-source deep multi-agent RL learning framework including state-of-the-art algorithms is opened.

1131 0

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

Ji Liu, T. Zhang, Qing Wang, Jiechao Xiong, Peng Sun, Xinghai Sun, Lei Han, Bo Li, Yang Zheng, Yongsheng Liu, Han Liu•Tue Sep 18 2018

This is the first public work to investigate AI agents that can defeat the built-in AI in the StarCraft II full game, and the AI agent TStarBot1 is based on deep reinforcement learning over a flat action structure and theAI agent T starBot2 isbased on hard-coded rules over a hierarchical action structure.

77 0

Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset

Anton Smerdov, Bo Zhou, P. Lukowicz, A. Somov•Sun Nov 01 2020

It is demonstrated that stress and concentration levels for professional players are less correlated, meaning more independent playstyle, and that the absence of team communication does not affect the professional players as much as amateur ones.

20 0

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

Chris Bamford, Sheng-Jun Huang, Santiago Ontañón, Lukasz Grela•Fri May 21 2021

Gym-JLRTS (pronounced “gym-micro-RTS”) is introduced as a fast-to-run RL environment for full-game RTS research and a collection of techniques to scale DRL to play full- game µRTS as well as ablation studies to demonstrate their empirical importance.

41 0

MSC: A Dataset for Macro-Management in StarCraft II

Kaiqi Huang, Huikai Wu, Junge Zhang•Sun Oct 08 2017

A baseline model is proposed and initial baseline results for global state evaluation and build order prediction are presented, which are two of the key tasks in macro-management.

17 0

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

C. L. Zitnick, Yuxin Wu, Yuandong Tian, Qucheng Gong, Wenling Shang•Fri Jun 30 2017

ELF, an Extensive, Lightweight and Flexible platform for fundamental reinforcement learning research, is proposed and it is shown that a network with Leaky ReLU and Batch Normalization coupled with long-horizon training and progressive curriculum beats the rule-based built-in AI more than $70\% of the time in the full game of Mini-RTS.

130 0

A Closer Look at Invalid Action Masking in Policy Gradient Algorithms

Shengyi Huang, Santiago Ontan'on•Wed Jun 24 2020

In recent years, Deep Reinforcement Learning (DRL) algorithms have achieved state-of-the-art performance in many challenging strategy games. Because these games have complicated rules, an action sampled from the full discrete action distribution predicted by the learned policy is likely to be invalid according to the game rules (e.g., walking into a wall). The usual approach to deal with this problem in policy gradient algorithms is to “mask out” invalid actions and just sample from the set of valid actions. The implications of this process, however, remain under-investigated. In this paper, we 1) show theoretical justification for such a practice, 2) empirically demonstrate its importance as the space of invalid actions grows, and 3) provide further insights by evaluating different action masking regimes, such as removing masking after an agent has been trained using masking.

445 0

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

Gabriel Synnaeve, Zeming Lin, Soumith Chintala, Nantas Nardelli, Nicolas Usunier, Timothée Lacroix, Alex Auvolat, Florian Richoux•Mon Oct 31 2016

This white paper argues for using RTS games as a benchmark for AI research, and describes the design and components of TorchCraft, a library that enables deep learning research on Real-Time Strategy games such as StarCraft: Brood War.

109 0

Experiments with Game Tree Search in Real-Time Strategy Games

Santiago Ontañón•Wed Aug 08 2012

RTMM, a real-time variant of the standard minimax algorithm, is presented and its applicability in the context of RTS games is discussed, its strengths and weaknesses are discussed, and it is evaluated in two real- time games.

8 0

Adding a benchmark result helps the community track progress.

Real-Time Strategy Games | State-of-the-Art