159 citations · 186 across the 16 of their papers we have counts for
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Evaluation Metrics for Safe Reinforcement Learning
Lindsay Spoor, Aske Plaat, Thomas Moerland
Safe reinforcement learning (RL) is commonly formalized as a Constrained Markov Decision Process (CMDP), in which an agent maximizes expected reward while keeping its expected cumu…
Guiding Skill Discovery with Foundation Models
Zhao Yang, Thomas M. Moerland, Mike Preuss +3
Learning diverse skills without hand-crafted reward functions could accelerate reinforcement learning in downstream tasks. However, existing skill discovery methods focus solely on…
Reset-free Reinforcement Learning with World Models
Zhao Yang, Thomas M. Moerland, Mike Preuss +2
Reinforcement learning (RL) is an appealing paradigm for training intelligent agents, enabling policy acquisition from the agent's own autonomously acquired experience. However, th…
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…