159 citations · 329 across the 29 of their papers we have counts for
4 papers · 1 filter
Model-based Reinforcement Learning: A Survey
Thomas M. Moerland, Joost Broekens, Aske Plaat +1
Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is a important challenge in artificial intelligence. Two key approaches to this probl…
A Unifying Framework for Reinforcement Learning and Planning
Thomas M. Moerland, Joost Broekens, Aske Plaat +1
Sequential decision making, commonly formalized as optimization of a Markov Decision Process, is a key challenge in artificial intelligence. Two successful approaches to MDP optimi…
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…
Think Too Fast Nor Too Slow: The Computational Trade-off Between Planning And Reinforcement Learning
Thomas M. Moerland, Anna Deichler, Simone Baldi +2
Planning and reinforcement learning are two key approaches to sequential decision making. Multi-step approximate real-time dynamic programming, a recently successful algorithm clas…