8 citations · 33 across the 15 of their papers we have counts for
10 papers · 1 filter
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…
Transfer Learning and Curriculum Learning in Sokoban
Zhao Yang, Mike Preuss, Aske Plaat
Transfer learning can speed up training in machine learning and is regularly used in classification tasks. It reuses prior knowledge from other tasks to pre-train networks for new…
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…
The Second Type of Uncertainty in Monte Carlo Tree Search
Thomas M Moerland, Joost Broekens, Aske Plaat +1
Monte Carlo Tree Search (MCTS) efficiently balances exploration and exploitation in tree search based on count-derived uncertainty. However, these local visit counts ignore a secon…
Warm-Start AlphaZero Self-Play Search Enhancements
Hui Wang, Mike Preuss, Aske Plaat
Recently, AlphaZero has achieved landmark results in deep reinforcement learning, by providing a single self-play architecture that learned three different games at super human lev…
A New Challenge: Approaching Tetris Link with AI
Matthias Muller-Brockhausen, Mike Preuss, Aske Plaat
Decades of research have been invested in making computer programs for playing games such as Chess and Go. This paper focuses on a new game, Tetris Link, a board game that is still…