14 citations · 29 across the 7 of their papers we have counts for
8 papers
Comparative Analysis of GAT and BERT for Human-Like Playtesting
Kleio Fragkedaki, Theodoros Panagiotakopoulos, Matteo Biasielli +1
Accurately modeling and understanding player experience is crucial for designing engaging puzzle games. To achieve this, a common approach involves collecting diverse user data to…
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…
Adaptive Warm-Start MCTS in AlphaZero-like Deep Reinforcement Learning
Hui Wang, Mike Preuss, Aske Plaat
AlphaZero has achieved impressive performance in deep reinforcement learning by utilizing an architecture that combines search and training of a neural network in self-play. Many r…
Tackling Morpion Solitaire with AlphaZero-likeRanked Reward Reinforcement Learning
Hui Wang, Mike Preuss, Michael Emmerich +1
Morpion Solitaire is a popular single player game, performed with paper and pencil. Due to its large state space (on the order of the game of Go) traditional search algorithms, suc…
Analysis of Hyper-Parameters for Small Games: Iterations or Epochs in Self-Play?
Hui Wang, Michael Emmerich, Mike Preuss +1
The landmark achievements of AlphaGo Zero have created great research interest into self-play in reinforcement learning. In self-play, Monte Carlo Tree Search is used to train a de…
Hyper-Parameter Sweep on AlphaZero General
Hui Wang, Michael Emmerich, Mike Preuss +1
Since AlphaGo and AlphaGo Zero have achieved breakground successes in the game of Go, the programs have been generalized to solve other tasks. Subsequently, AlphaZero was developed…