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Rolling Horizon Evolutionary Algorithms for General Video Game Playing

Raluca’s research is focused on Rolling Horizon Evolutionary Algorithms (RHEA), which show promise to outperform the dominating Monte Carlo Tree Search in the area of general video game playing. As a sub-field of Artificial Intelligence, general video game playing aims to design an agent which would achieve high-level play in any given game, thus raising the need to generalize the heuristics used and introduce various machine learning techniques to gather information about the previously unknown game. So far, various aspects have been explored in several games, such as the impact of the hyper parameters on performance; population seeding techniques; and other algorithm structure modifications previously encountered in literature, now tested in a consistent environment and a general setting. On successful completion, the research aims to bring forward better NPCs and new challenging experiences for players, as well as reliable game testing tools.

Raluca’s latest studies look at a better understanding of RHEA’s inner workings through feature analysis (including algorithm convergence, sense of danger or fitness landscapes observed), as well as performance prediction based on the agent’s experience while playing the game.

Related publications:

Full publication list


  • AAAI Workshop on Knowledge Extraction from Games (KEG) 2019
  • IEEE Congress on Evolutionary Computation (CEC) 2019
  • IEEE Transactions on Games (TOG) 2018
  • IEEE Foundations of Digital Games (FDG) 2018
  • IEEE Conference on Computational Intelligence and Games (CIG) 2018
  • IEEE Congress on Evolutionary Computation (CEC) 2018
  • AAAI Workshop on Knowledge Extraction from Games (KEG) 2018
  • IEEE Congress on Evolutionary Computation (CEC) 2017