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About

Raluca D. Gaina is a Lecturer in Game AI at Queen Mary University of London from June 2021. She got her Ph.D. in Intelligent Games and Games Intelligence from Queen Mary University of London in May 2021, in the area of rolling horizon evolution in general video game playing, after completing a B.Sc. and M.Sc. in Computer Games at the University of Essex in 2015 and 2016, respectively. In 2018, she did a 3 month internship at Microsoft Research Cambridge, working on the Multi-Agent Reinforcement Learning in Malmo Competition (MARLO). She was the track organiser of the Two-Player General Video Game AI Competition (GVGAI) 2016-2019 and is the Vice-Chair for Conferences of the IEEE CIS Games Technical Committee in 2020. Her research interests include general video game playing AI, reinforcement learning and evolutionary computation algorithms.

Read an interview about my PhD experience here.

Research Topic

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. She’s further studied the offline and online adaptation of RHEA (and all of its various parameters and modifications) for improved performance.

Related publications: Full publication list

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GAIG Publications

2024

2023

2022

2021

• PhD 2021 Rolling Horizon Evolutionary Algorithms for General Video Game Playing
  Raluca D. Gaina, PhD Thesis, Queen Mary University of London, UK, May, 2021.
  pubs-2021  thesis  ai  games  rhea  evolutionary-algorithms  optimisation
  
  
• AIIDE 2021 TAG: Terraforming Mars
  Raluca D. Gaina and James Goodman and Diego Perez-Liebana, in Proceedings of the 17th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2021.
  pubs-2021  aiide  game-analysis  tabletop-games  general-game-playing  rhea  mcts  terraforming-mars
  
  

2020

• IEEE TOG 2020 Rolling Horizon Evolutionary Algorithms for General Video Game Playing
  Raluca D. Gaina and Sam Devlin and Diego Perez-Liebana and Simon M. Lucas, in arxiv:2003.12331, 2020.
  pubs-2020  gvgai  general-video-game-playing  ieee-tog  rhea  optimisation  ntbea
  
  
• IEEE COG 2020 Self-Adaptive Rolling Horizon Evolutionary Algorithms for General Video Game Playing
  Raluca D. Gaina and Chiara F. Sironi and Mark H.M. Winands and Diego Perez-Liebana and Simon M. Lucas, in IEEE Conference on Games (CoG), 2020.
  pubs-2020  ieee-cog  gvgai  general-vide-game-playing  rhea  optimisation
  
  
• IEEE COG 2020 Rolling Horizon NEAT for General Video Game Playing
  Diego Perez-Liebana and Muhammad Sajid Alam and Raluca D. Gaina, in IEEE Conference on Games (CoG), 2020.
  pubs-2020  ieee-cog  gvgai  general-vide-game-playing  rhea  neat
  
  
• EXAG @ AIIDE 2020 TAG: A Tabletop Games Framework
  Raluca D. Gaina and Martin Balla and Alexander Dockhorn and Raul Montoliu and Diego Perez-Liebana, in Experimental AI in Games (EXAG), AIIDE 2020 Workshop, 2020.
  pubs-2020  aiide  exag  tabletop-games  general-game-playing  rhea  mcts
  
  
• Evo* 2020 Efficient Heuristic Policy Optimisation for a Challenging Strategic Card Game
  Raul Montoliu and Raluca D. Gaina and Diego Perez-Liebana and Daniel Delgado and Simon M. Lucas, in International Conference on the Applications of Evolutionary Computation (EvoStar), 12104, pp. 403-418, 2020.
  pubs-2020  optimisation  evo-star  ntbea  card-games  oep  emcts
  
  
• AIIDE 2020 Tribes: A New Turn-Based Strategy Game for AI
  Diego Perez Liebana and Yu-Jhen Hsu and Stavros Emmanouilidis and Bobby Khaleque and Raluca Gaina, in Sixteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-20), 2020.
  pubs-2020  aiide  tribes  polytopia  rhea  mcts
  
  
• 2020 Design and Implemenation of TAG: A Tabletop Games Framework
  Raluca D. Gaina and Martin Balla and Alexander Dockhorn and Raul Montoliu and Diego Perez-Liebana, in arxiv:2009.12065, 2020.
  pubs-2020  arxiv  tabletop-games  general-game-playing  rhea  mcts
  
  
• 2020 Abstract Forward Models
  Diego Perez-Liebana and Raluca D. Gaina and Tommy Thompson, in Artificial and Computational Intelligence in Games: Revolutions in Computational Game AI (Dagstuhl Seminar 19511), 9, pp. 67-114, 2020.
  pubs-2020  dagstuhl  abstract  forward-model  mcts  rhea  pommerman
  
  

2019

• IEEE TOG 2019 General Video Game AI: a Multi-Track Framework for Evaluating Agents Games and Content Generation Algorithms
  Diego Perez-Liebana and Jialin Liu and Ahmed Khalifa and Raluca D. Gaina and Julian Togelius and Simon M. Lucas, in IEEE Transactions on Games, 11, pp. 195-214, Sep, 2019.
  pubs-2019  gvgai  general-video-game-playing  ieee-tog
  
  
• IEEE COG 2019 Learning Local Forward Models on Unforgiving Games
  Alexander Dockhorn and Simon M Lucas and Vanessa Volz and Ivan Bravi and Raluca D. Gaina and Diego Perez-Liebana, in IEEE Conference on Games (COG), pp. 1-4, 2019.
  pubs-2019  ieee-cog  forward-model-learning
  
  
• IEEE COG 2019 Optimising Level Generators for General Video Game AI
  Olve Drageset and Raluca D. Gaina and Diego Perez-Liebana and Mark H.M. Winands, in IEEE Conference on Games (COG), pp. 1-8, 2019.
  pubs-2019  gvgai  ieee-cog  level-generation  procedural-content-generation
  
  
• IEEE COG 2019 'Did You Hear That?' Learning to Play Video Games from Audio Cues
  Raluca D. Gaina and Matthew Stephenson, in IEEE Conference on Games (COG), pp. 1-4, 2019.
  pubs-2019  audio  gvgai  general-video-game-playing  ieee-cog  reinforcement-learning
  
  
• IEEE COG 2019 Project Thyia: A Forever Gameplayer
  Raluca D. Gaina and Simon M. Lucas and Diego Perez-Liebana, in IEEE Conference on Games (COG), pp. 1-8, 2019.
  pubs-2019  gvgai  general-video-game-playing  ieee-cog  rhea
  
  
• IEEE COG 2019 A Local Approach to Forward Model Learning: Results on the Game of Life Game
  Simon M Lucas and Alexander Dockhorn and Vanessa Volz and Chris Bamford and Raluca D. Gaina and Ivan Bravi and Diego Perez-Liebana and Sanaz Mostaghim and Rudolf Kruse, in IEEE Conference on Games (COG), pp. 1-8, 2019.
  pubs-2019  ieee-cog  forward-model-learning
  
  
• GameSim @ AAAI 2019 Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best
  Simon M Lucas and Jialin Liu and Ivan Bravi and Raluca D. Gaina and John Woodward and Vanessa Volz and Diego Perez-Liebana, in Game Simulations Workshop (AAAI), 2019.
  pubs-2019  aaai  rhea  optimisation  ntbea
  
  
• BOOK 2019 General Video Game Artificial Intelligence
  Diego Perez-Liebana and Simon M. Lucas and Raluca D. Gaina and Julian Togelius and Ahmed Khalifa and Jialin Liu, 2019.
  pubs-2019  gvgai  general-video-game-playing  book
  
  
• AIIDE 2019 Analysis of Statistical Forward Planning Methods in Pommerman
  Diego Perez-Liebana and Raluca D. Gaina and Olve Drageset and Ercument Ilhan and Martin Balla and Simon M. Lucas, in Proceedings of the Artificial intelligence and Interactive Digital Entertainment (AIIDE), 15, pp. 66-72, 2019.
  pubs-2019  aiide  pommerman  rhea  mcts
  
  
• AAAI 2019 Tackling Sparse Rewards in Real-Time Games with Statistical Forward Planning Methods
  Raluca D. Gaina and Simon M. Lucas and Diego Perez-Liebana, in AAAI Conference on Artificial Intelligence (AAAI-19), 33, pp. 1691-1698, 2019.
  pubs-2019  sparse-rewards  gvgai  general-video-game-playing  aaai  rhea  mcts
  
  

2018

• IEEE COG 2018 General Win Prediction from Agent Experience
  Raluca D. Gaina and Simon M. Lucas and Diego Perez-Liebana, in Proc. of the IEEE Conference on Computational Intelligence and Games (CIG), pp. 1-8, Aug, 2018.
  pubs-2018  gvgai  general-video-game-playing  win-prediction  ieee-cog  rhea  mcts
  
  
• Evo* 2018 Self-adaptive MCTS for General Video Game Playing
  Chiara F. Sironi and Jialin Liu and Diego Perez-Liebana and Raluca D. Gaina and Ivan Bravi and Simon M. Lucas and Mark H. M. Winands, in Applications of Evolutionary Computation, pp. 358-375, 2018.
  pubs-2018  gvgai  general-video-game-playing  optimisation  evo-star  mcts
  
  
• AIIDE 2018 VERTIGO: Visualisation of Rolling Horizon Evolutionary Algorithms in GVGAI
  Raluca D. Gaina and Simon M. Lucas and Diego Perez-Liebana, in The 14th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 265-267, 2018.
  pubs-2018  gvgai  general-video-game-playing  aiide  visualisation
  
  
• 2018 Procedural Content Generation for Minecraft
  Christoph Salge and Alan Blair and Alex J. Champandard and Michael Cook and Raluca D. Gaina and Diego Perez-Liebana and Jacob Shrum and Emily Short and Vanessa Volz, in Artificial and Computational Intelligence in Games: Revolutions in Computational Game AI (Dagstuhl Seminar 19511), 9, pp. 67-114, 2018.
  pubs-2020  dagstuhl  minecraft  procedural-content-generation
  
  

2017