13 papers
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…
EfficientTDMPC: Improved MPC Objectives for Sample-Efficient Continuous Control
Thomas Evers, Cristian Meo, Wendelin Bohmer +2
We introduce EfficientTDMPC, a sample-efficient model-based reinforcement learning method for continuous control built on the TD-MPC family of algorithms. Central to this family is…
On the Equivalence of Random Network Distillation, Deep Ensembles, and Bayesian Inference
Moritz A. Zanger, Yijun Wu, Pascal R. Van der Vaart +2
Uncertainty quantification is central to safe and efficient deployments of deep learning models, yet many computationally practical methods lack lacking rigorous theoretical motiva…
Sparse Masked Attention Policies for Reliable Generalization
Caroline Horsch, Laurens Engwegen, Max Weltevrede +2
In reinforcement learning, abstraction methods that remove unnecessary information from the observation are commonly used to learn policies which generalize better to unseen tasks.…
Value Improved Actor Critic Algorithms
Yaniv Oren, Moritz A. Zanger, Pascal R. van der Vaart +3
To learn approximately optimal acting policies for decision problems, modern Actor Critic algorithms rely on deep Neural Networks (DNNs) to parameterize the acting policy and greed…
How Ensembles of Distilled Policies Improve Generalisation in Reinforcement Learning
Max Weltevrede, Moritz A. Zanger, Matthijs T. J. Spaan +1
In the zero-shot policy transfer setting in reinforcement learning, the goal is to train an agent on a fixed set of training environments so that it can generalise to similar, but…