59 citations · 110 across the 49 of their papers we have counts for
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Potential-based Reward Shaping in Sokoban
Zhao Yang, Mike Preuss, Aske Plaat
Learning to solve sparse-reward reinforcement learning problems is difficult, due to the lack of guidance towards the goal. But in some problems, prior knowledge can be used to aug…
High-Accuracy Model-Based Reinforcement Learning, a Survey
Aske Plaat, Walter Kosters, Mike Preuss
Deep reinforcement learning has shown remarkable success in the past few years. Highly complex sequential decision making problems from game playing and robotics have been solved w…
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…
Visualizing MuZero Models
Joery A. de Vries, Ken S. Voskuil, Thomas M. Moerland +1
MuZero, a model-based reinforcement learning algorithm that uses a value equivalent dynamics model, achieved state-of-the-art performance in Chess, Shogi and the game of Go. In con…