Real-Time Strategy Games

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

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Datasets archive 2025-07-28

5 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

2 subtasks in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

24 shown of 24 papers with code (46 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• The StarCraft Multi-Agent Challenge
  11 Feb 2019 23 repositories listed Syntology ran 6 of 15 samples · 9 unverified · 13 pointer-only (licence)
  In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap.
• StarCraft II: A New Challenge for Reinforcement Learning
  16 Aug 2017 10 repositories listed Syntology ran 5 of 5 samples · 0 unverified · 2 pointer-only (licence)
  Finally, we present initial baseline results for canonical deep reinforcement learning agents applied to the StarCraft II domain.
• Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning
  21 May 2021 5 repositories listed
  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…
• Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset
  2 Nov 2020 3 repositories listed
  An important feature of the dataset is simultaneous data collection from five players, which facilitates the analysis of sensor data on a team level.
• Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games
  5 Oct 2020 3 repositories listed
  Training agents using Reinforcement Learning in games with sparse rewards is a challenging problem, since large amounts of exploration are required to retrieve even the first reward.
• A Closer Look at Invalid Action Masking in Policy Gradient Algorithms
  25 Jun 2020 2 repositories listed Syntology ran 2 of 5 samples · 3 unverified · 1 pointer-only (licence)
  In recent years, Deep Reinforcement Learning (DRL) algorithms have achieved state-of-the-art performance in many challenging strategy games.
• TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game
  19 Sep 2018 2 repositories listed
  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…
• MSC: A Dataset for Macro-Management in StarCraft II
  9 Oct 2017 2 repositories listed
  We also split MSC into training, validation and test set for the convenience of evaluation and comparison.
• ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games
  4 Jul 2017 2 repositories listed
  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…
• TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games
  1 Nov 2016 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  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…
• Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis
  30 Aug 2024 1 repository listed
  The accuracy of the observed strength relations in these games is comparable to traditional pairwise win value predictions, while also offering a more manageable complexity for analysis.
• Terrain Analysis in StarCraft 1 and 2 as Combinatorial Optimization
  18 May 2022 1 repository listed
  The goal of terrain analysis is to gather and process data about the map topology and properties to have a qualitative spatial representation.
• Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning
  29 Nov 2020 1 repository listed
  In this article, we propose the methods based on the sensor data analysis for predicting whether a player will win the future encounter.
• Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy games
  3 Jan 2019 1 repository listed
  However, few Constraint Programming formalisms can deal with both optimization and uncertainty at the same time, and none of them are convenient to model problems we tackle in this paper.
• StarAlgo: A Squad Movement Planning Library for StarCraft using Monte Carlo Tree Search and Negamax
  29 Dec 2018 1 repository listed
  Real-Time Strategy (RTS) games have recently become a popular testbed for artificial intelligence research.
• Macro action selection with deep reinforcement learning in StarCraft
  2 Dec 2018 1 repository listed
  These rules are not scalable and efficient enough to cope with the enormous yet partially observed state space in the game.
• Clear the Fog: Combat Value Assessment in Incomplete Information Games with Convolutional Encoder-Decoders
  30 Nov 2018 1 repository listed
  StarCraft, one of the most popular real-time strategy games, is a compelling environment for artificial intelligence research for both micro-level unit control and macro-level strategic decision making.
• Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger
  30 Nov 2018 1 repository listed
  We formulate the problem of defogging as state estimation and future state prediction from previous, partial observations in the context of real-time strategy games.
• Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games
  15 Aug 2018 1 repository listed
  Reinforcement learning (RL) is an area of research that has blossomed tremendously in recent years and has shown remarkable potential for artificial intelligence based opponents in computer games.
• StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer Learning
  3 Apr 2018 1 repository listed
  With reinforcement learning and curriculum transfer learning, our units are able to learn appropriate strategies in StarCraft micromanagement scenarios.
• Multi-platform Version of StarCraft: Brood War in a Docker Container: Technical Report
  7 Jan 2018 1 repository listed
  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…
• STARDATA: A StarCraft AI Research Dataset
  7 Aug 2017 1 repository listed
  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
  19 Nov 2012 1 repository listed
  We evaluated this clustering method by predicting the outcomes of battles based on armies compositions' mixtures components
• Experiments with Game Tree Search in Real-Time Strategy Games
  9 Aug 2012 1 repository listed
  Game tree search algorithms such as minimax have been used with enormous success in turn-based adversarial games such as Chess or Checkers.

Syntology lines on 4 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.