Papers Real-Time Strategy Games
“Real-Time Strategy Games” 태그가 달린 논문 46편 · 필터 해제
Three-dimensional attention Transformer for state evaluation in real-time strategy games
Situation assessment in Real-Time Strategy (RTS) games is crucial for understanding decision-making in complex adversarial environments. However, existing methods remain limited in processing multi-dimensional feature in…
Decision MakingReal-Time Strategy GamesHuman-like Bots for Tactical Shooters Using Compute-Efficient Sensors
Artificial intelligence (AI) has enabled agents to master complex video games, from first-person shooters like Counter-Strike to real-time strategy games such as StarCraft II and racing games like Gran Turismo. While the…
CPUImitation LearningReal-Time Strategy GamesStarcraft+1Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis
How can balance be quantified in game settings? This question is crucial for game designers, especially in player-versus-player (PvP) games, where analyzing the strength relations among predefined team compositions-such …
Card GamesGame DesignQuantizationReal-Time Strategy Games+1CortexCompile: Harnessing Cortical-Inspired Architectures for Enhanced Multi-Agent NLP Code Synthesis
Current approaches to automated code generation often rely on monolithic models that lack real-time adaptability and scalability. This limitation is particularly evident in complex programming tasks that require dynamic …
Code GenerationReal-Time Strategy GamesDeep Reinforcement Learning for Autonomous Cyber Defence: A Survey
The rapid increase in the number of cyber-attacks in recent years raises the need for principled methods for defending networks against malicious actors. Deep reinforcement learning (DRL) has emerged as a promising appro…
BenchmarkingDeep Reinforcement LearningReal-Time Strategy Gamesreinforcement-learning+2A Hierarchical Game-Theoretic Decision-Making for Cooperative Multi-Agent Systems Under the Presence of Adversarial Agents
Underlying relationships among Multi-Agent Systems (MAS) in hazardous scenarios can be represented as Game-theoretic models. This paper proposes a new hierarchical network-based model called Game-theoretic Utility Tree (…
Decision MakingReal-Time Strategy GamesSystem Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games
As Artificial and Robotic Systems are increasingly deployed and relied upon for real-world applications, it is important that they exhibit the ability to continually learn and adapt in dynamically-changing environments, …
Continual LearningLifelong learningReal-Time Strategy Gamesreinforcement-learning+2Forecasting Evolution of Clusters in Game Agents with Hebbian Learning
Large multi-agent systems such as real-time strategy games are often driven by collective behavior of agents. For example, in StarCraft II, human players group spatially near agents into a team and control the team to de…
ClusteringMulti-agent Reinforcement LearningReal-Time Strategy GamesSelf-Supervised Learning+2Terrain Analysis in StarCraft 1 and 2 as Combinatorial Optimization
Terrain analysis in Real-Time Strategy games is a necessary step to allow spacial reasoning. The goal of terrain analysis is to gather and process data about the map topology and properties to have a qualitative spatial …
Combinatorial OptimizationReal-Time Strategy GamesStarcraftOn games and simulators as a platform for development of artificial intelligence for command and control
Games and simulators can be a valuable platform to execute complex multi-agent, multiplayer, imperfect information scenarios with significant parallels to military applications: multiple participants manage resources and…
Real-Time Strategy GamesStarcraftStarcraft IIMulti-Agent Deep Reinforcement Learning using Attentive Graph Neural Architectures for Real-Time Strategy Games
In real-time strategy (RTS) game artificial intelligence research, various multi-agent deep reinforcement learning (MADRL) algorithms are widely and actively used nowadays. Most of the research is based on StarCraft II e…
Deep Reinforcement LearningGraph AttentionReal-Time Strategy GamesReinforcement Learning (RL)+2Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning
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 player…
Deep Reinforcement LearningGPUReal-Time Strategy Gamesreinforcement-learning+4Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning
Current research in eSports lacks the tools for proper game practising and performance analytics. The majority of prior work relied only on in-game data for advising the players on how to perform better. However, in-game…
BIG-bench Machine LearningInterpretable Machine LearningMultimodal Deep LearningPerson Re-Identification+6Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset
Proper training and analytics in eSports require accurately collected and annotated data. Most eSports research focuses exclusively on in-game data analysis, and there is a lack of prior work involving eSports athletes' …
Feature ImportancePerson Re-IdentificationPhysiological ComputingReal-Time Strategy Games+4Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games
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. To tackle this problem, a common appr…
Real-Time Strategy GamesReinforcement Learning (RL)The Design Of "Stratega": A General Strategy Games Framework
Stratega, a general strategy games framework, has been designed to foster research on computational intelligence for strategy games. In contrast to other strategy game frameworks, Stratega allows to create a wide variety…
Decision MakingReal-Time Strategy GamesA Closer Look at Invalid Action Masking in Policy Gradient Algorithms
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 dis…
Deep Reinforcement LearningReal-Time Strategy GamesvalidA Survey of Deep Reinforcement Learning in Video Games
Deep reinforcement learning (DRL) has made great achievements since proposed. Generally, DRL agents receive high-dimensional inputs at each step, and make actions according to deep-neural-network-based policies. This lea…
Deep Reinforcement LearningReal-Time Strategy Gamesreinforcement-learningReinforcement Learning+2The StarCraft Multi-Agent Challenge
In the last few years, deep multi-agent reinforcement learning (RL) has become a highly active area of research. A particularly challenging class of problems in this area is partially observable, cooperative, multi-agent…
BenchmarkingMuJoCoMulti-agent Reinforcement LearningReal-Time Strategy Games+6Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy games
Decision-making problems can be modeled as combinatorial optimization problems with Constraint Programming formalisms such as Constrained Optimization Problems. However, few Constraint Programming formalisms can deal wit…
Combinatorial OptimizationDecision MakingReal-Time Strategy Games