6 papers
Minibal: Balanced Game-Playing Without Opponent Modeling
Quentin Cohen-Solal, Tristan Cazenave
Recent advances in game AI, such as AlphaZero and Athénan, have achieved superhuman performance across a wide range of board games. While highly powerful, these agents are ill-sui…
Generalized Rapid Action Value Estimation in Memory-Constrained Environments
Aloïs Rautureau, Tristan Cazenave, Ãric Piette
Generalized Rapid Action Value Estimation (GRAVE) has been shown to be a strong variant within the Monte-Carlo Tree Search (MCTS) family of algorithms for General Game Playing (GGP…
On some improvements to Unbounded Minimax
Quentin Cohen-Solal, Tristan Cazenave
This paper presents the first experimental evaluation of four previously untested modifications of Unbounded Best-First Minimax algorithm. This algorithm explores the game tree by…
Enhancing Reinforcement Learning Through Guided Search
Jérôme Arjonilla, Abdallah Saffidine, Tristan Cazenave
With the aim of improving performance in Markov Decision Problem in an Off-Policy setting, we suggest taking inspiration from what is done in Offline Reinforcement Learning (RL). I…
Perfect Information Monte Carlo with Postponing Reasoning
Jérôme Arjonilla, Abdallah Saffidine, Tristan Cazenave
Imperfect information games, such as Bridge and Skat, present challenges due to state-space explosion and hidden information, posing formidable obstacles for search algorithms. Det…
Deep Reinforcement Learning for 5*5 Multiplayer Go
Brahim Driss, Jérôme Arjonilla, Hui Wang +2
In recent years, much progress has been made in computer Go and most of the results have been obtained thanks to search algorithms (Monte Carlo Tree Search) and Deep Reinforcement…