Deep RTS: A Game Environment For Deep Reinforcement Learning In Real-time Strategy Games

Abstract

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. This success is primarily due to the vast capabilities of convolutional neural networks, that can extract useful features from noisy and complex data. Games are excellent tools to test and push the boundaries of novel RL algorithms because they give valuable insight into how well an algorithm can perform in isolated environments without the real-life consequences. Real-time strategy games (RTS) is a genre that has tremendous complexity and challenges the player in short and long-term planning. There is much research that focuses on applied RL in RTS games, and novel advances are therefore anticipated in the not too distant future. However, there are to date few environments for testing RTS AIs. Environments in the literature are often either overly simplistic, such as microRTS, or complex and

Stats

• citations41
• S2 citations—
• github stars249
• HF likes0
• heat score16.97
• arxiv keyandersen2018deep

Related papers

• Eden: A Unified Environment Framework For Booming Reinforcement Learning Algorithms (2021)0.00
• Statistical Reinforcement Learning In The Real World: A Survey Of Challenges And Future Directions (2026)0.00
• Multi-agent Deep Reinforcement Learning Using Attentive Graph Neural Architectures For Real-time Strategy Games (2021)7.16
• Learning To Design Games: Strategic Environments In Reinforcement Learning (2017)0.00
• Toybox: A Suite Of Environments For Experimental Evaluation Of Deep Reinforcement Learning (2019)0.00
• A Survey Of Deep Reinforcement Learning In Video Games (2019)0.00
• A Competition Winning Deep Reinforcement Learning Agent In Microrts (2024)3.58
• An Empirical Investigation Of The Challenges Of Real-world Reinforcement Learning (2020)0.00