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
Mario AI
eSports Sensors Dataset
StarData
Latest papers
Terrain Analysis in StarCraft 1 and 2 as Combinatorial Optimization
The goal of terrain analysis is to gather and process data about the map topology and properties to have a qualitative spatial representation.
Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning
farama-foundation/microrts-py
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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.
Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning
In this article, we propose the methods based on the sensor data analysis for predicting whether a player will win the future encounter.
Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset
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
farama-foundation/microrts-py
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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
Stable-Baselines-Team/stable-baselines3-contrib
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In recent years, Deep Reinforcement Learning (DRL) algorithms have achieved state-of-the-art performance in many challenging strategy games.
The StarCraft Multi-Agent Challenge
oxwhirl/pymarl
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In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap.
Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy games
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
Real-Time Strategy (RTS) games have recently become a popular testbed for artificial intelligence research.
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.
