Real-Time Strategy Games
23 papers with code • 0 benchmarks • 4 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 )
Benchmarks
These leaderboards are used to track progress in Real-Time Strategy Games
Most implemented papers
Experiments with Game Tree Search in Real-Time Strategy Games
Game tree search algorithms such as minimax have been used with enormous success in turn-based adversarial games such as Chess or Checkers.
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
STARDATA: A StarCraft AI Research Dataset
We provide full game state data along with the original replays that can be viewed in StarCraft.
Multi-platform Version of StarCraft: Brood War in a Docker Container: Technical Report
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 StarCraft bots.
StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer Learning
With reinforcement learning and curriculum transfer learning, our units are able to learn appropriate strategies in StarCraft micromanagement scenarios.
Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games
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.
Clear the Fog: Combat Value Assessment in Incomplete Information Games with Convolutional Encoder-Decoders
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
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.
Macro action selection with deep reinforcement learning in StarCraft
These rules are not scalable and efficient enough to cope with the enormous yet partially observed state space in the game.
StarAlgo: A Squad Movement Planning Library for StarCraft using Monte Carlo Tree Search and Negamax
Real-Time Strategy (RTS) games have recently become a popular testbed for artificial intelligence research.