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A Local Approach to Forward Model Learning: Results on the Game of Life Game

, , , , , , , and . 2019 IEEE Conference on Games (CoG), page 1--8. (August 2019)
DOI: 10.1109/CIG.2019.8848002

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Rinascimento: Optimising Statistical Forward Planning Agents for Playing Splendor., , , and . CoG, page 1-8. IEEE, (2019)Learning Local Forward Models on Unforgiving Games, , , , , and . 2019 IEEE Conference on Games (CoG), (August 2019)Rinascimento: Playing Splendor-Like Games With Event-Value Functions., and . IEEE Trans. Games, 15 (1): 16-25 (March 2023)Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best., , , , , , and . CoRR, (2019)Evolving Game-Specific UCB Alternatives for General Video Game Playing., , , and . EvoApplications (1), volume 10199 of Lecture Notes in Computer Science, page 393-406. (2017)A Local Approach to Forward Model Learning: Results on the Game of Life Game., , , , , , , , and . CoG, page 1-8. IEEE, (2019)Rinascimento: using event-value functions for playing Splendor., and . CoG, page 283-290. IEEE, (2020)Game AI hyperparameter tuning in rinascimento., , and . GECCO (Companion), page 1742-1746. ACM, (2019)A Local Approach to Forward Model Learning: Results on the Game of Life Game, , , , , , , and . 2019 IEEE Conference on Games (CoG), page 1--8. (August 2019)Learning Local Forward Models on Unforgiving Games., , , , , and . CoG, page 1-4. IEEE, (2019)