From the 1 of 23 linked papers with an AI index.
23 papers
Optimizing Minimax Regret in Uncertain MDPs with Small Sets of Policies
Sterre Lutz, Daniël Vos, Matthijs T. J. Spaan +1
Sequential decision-making in real-world applications often involves uncertainty about the environment's model. Uncertain Markov decision processes (UMDPs) represent the possible e…
Training on Irrelevant States Implies Data Augmentation: Generalization in Contextual MDPs
Max Weltevrede, Caroline Horsch, Matthijs T. J. Spaan +1
The paper shows that training reinforcement‑learning agents on additional, irrelevant states acts like data augmentation and can improve zero‑shot generalization in contextual MDPs…
Generalization in offline RL: The structure is more important than the amount of pessimism
Max Weltevrede, Matthijs T. J. Spaan, Wendelin Böhmer
While pessimism counteracts overestimation bias in offline reinforcement learning (RL), being overly conservative has been associated with hindering certain forms of generalization…
COP-Q: Safety-First Reinforcement Learning for Robot Control via Cholesky-Ordered Projection
Guopeng Li, Moritz A. Zanger, Matthijs T. J. Spaan +1
Safe robot control requires maximizing return while satisfying safety constraints. In off-policy safe reinforcement learning, reward and safety Q-values are commonly learned by sep…
PMCTS: Particle Monte Carlo Tree Search for Principled Parallelized Inference Time Scaling
Yaniv Oren, Viliam Vadocz, Joery A. de Vries +3
Monte Carlo Tree Search (MCTS) is a widely used approach for policy improvement through search with increasing popularity for real world applications. Due to the sequential and det…
Twice Sequential Monte Carlo for Tree Search
Yaniv Oren, Joery A. de Vries, Pascal R. van der Vaart +2
Model-based reinforcement learning (RL) methods that leverage search are responsible for many milestone breakthroughs in RL. Sequential Monte Carlo (SMC) recently emerged as an alt…