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 with no code
Deep Reinforcement Learning for Autonomous Cyber Operations: A Survey
An overview of state-of-the-art approaches for scaling DRL to domains that confront learners with the curse of dimensionality, and; iv.)
A Hierarchical Game-Theoretic Decision-Making for Cooperative Multi-Agent Systems Under the Presence of Adversarial Agents
We present an Explore game domain, where we measure the performance of MAS achieving tasks from the perspective of balancing the success probability and system costs.
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games
In this paper, we introduce the Lifelong Reinforcement Learning Components Framework (L2RLCF), which standardizes L2RL systems and assimilates different continual learning components (each addressing different aspects of the lifelong learning problem) into a unified system.
Forecasting Evolution of Clusters in Game Agents with Hebbian Learning
In this light, clustering the agents in the game has been used for various purposes such as the efficient control of the agents in multi-agent reinforcement learning and game analytic tools for the game users.
On 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 make decisions that command assets to secure specific areas of a map or neutralize opposing forces.
Multi-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.
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.
A Survey of Deep Reinforcement Learning in Video Games
In this paper, we survey the progress of DRL methods, including value-based, policy gradient, and model-based algorithms, and compare their main techniques and properties.
High-Level Strategy Selection under Partial Observability in StarCraft: Brood War
We consider the problem of high-level strategy selection in the adversarial setting of real-time strategy games from a reinforcement learning perspective, where taking an action corresponds to switching to the respective strategy.
Evolutionary Multi-objective Optimization of Real-Time Strategy Micro
We believe that our results indicate the usefulness of potential fields as a representation, and of evolutionary multi-objective optimization as an approach, for generating good micro.
