Strategy Game AI

TNT members involved in this project:
Jun.-Prof. Dr.-Ing. Alexander Dockhorn

Each game genre plays differently and comes with its unique challenges. Studying and possibly overcoming these challenges tells us more about the design of machine learning agents under special conditions. In this project, we focus on developing Strategy Game AI. In strategy games, we are often tasked to control multiple units at the same time. This yields a combinatorial action space which grows exponentially with the number of units under our control. Even modern AI solutions cannot handle this explosion of the search space without special modifications. We at the TNT, study how machine learning-based agents can overcome this challenge and learn to play strategy games proficiently.

The original search space of strategy games is often too large to handle. Therefore, methods of abstraction are required to reduce the complexity of said search space and allow agents to quickly identify good actions. Mechanisms of abstraction include generating approximate homomorphisms of the original game, shaping rewards, filtering/clustering actions or states, and time-based abstractions such as the option learning framework. All those can drastically improve the learning speed of an AI agent. While strategy games provide us with interesting benchmark tasks to test these methods, abstraction is not limited to said domain and similar techniques can often be transferred to other machine-learning tasks.

Show recent publications only
  • Conference Contributions
    • Linjie Xu, Jorge Hurtado-Grueso, Dominic Jeurissen, Diego Perez Liebana, Alexander Dockhorn
      Elastic Monte Carlo Tree Search State Abstraction for Strategy Game Playing
      2022 IEEE Conference on Games (CoG), IEEE, 2022
    • Linjie Xu, Diego Perez-Liebana, Alexander Dockhorn
      Towards Applicable State Abstractions: a Preview in Strategy
      The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM) - RL as a Model of Agency, pp. 1-7, 2022
    • Alexander Dockhorn, Jorge Hurtado-Grueso, Dominik Jeurissen, Linjie Xu, Diego Perez-Liebana
      Game State and Action Abstracting Monte Carlo Tree Search for General Strategy Game-Playing
      Proceedings of the 2021 IEEE Conference on Games (CoG), IEEE, pp. 1--8, August 2021
    • Alexander Dockhorn, Jorge Hurtado-Grueso, Dominik Jeurissen, Linjie Xu, Diego Perez-Liebana
      Portfolio Search and Optimization for General Strategy Game-Playing
      2021 IEEE Congress on Evolutionary Computation (CEC), IEEE, pp. 2085-2092, 2021
    • Diego Perez-Liebana, Cristina Guerrero-Romero, Alexander Dockhorn, Linjie Xu, Jorge Hurtado, Dominik Jeurissen
      Generating Diverse and Competitive Play-Styles for Strategy Games
      2021 IEEE Conference on Games (CoG), IEEE, pp. 1-8, 2021
    • Diego Perez-Liebana, Alexander Dockhorn, Jorge Hurtado Grueso, Dominik Jeurissen
      The Design Of “Stratega”: A General Strategy Games Framework
      arXiv:2009.05643, pp. 1--7, 2020
    • Alexander Dockhorn, Jorge Hurtado Grueso, Dominik Jeurissen, Diego Perez-Liebana
      “Stratega”: A General Strategy Games Framework
      Joint Proceedings of the AIIDE 2020 Workshops co-located with 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020); Artificial Intelligence for Strategy Games, CEUR Workshop Proceedings, pp. 1--7, Worcester, 2020, edited by Osborn, Joesph C.