35 citations · 132 across the 27 of their papers we have counts for
5 papers · 1 filter
Efficient Learning for Entropy-Regularized Markov Decision Processes via Multilevel Monte Carlo
Matthieu Meunier, Christoph Reisinger, Yufei Zhang
Designing efficient learning algorithms with complexity guarantees for Markov decision processes (MDPs) with large or continuous state and action spaces remains a fundamental chall…
A Neural RDE approach for continuous-time non-Markovian stochastic control problems
Melker Hoglund, Emilio Ferrucci, Camilo Hernandez +4
We propose a novel framework for solving continuous-time non-Markovian stochastic control problems by means of neural rough differential equations (Neural RDEs) introduced in Morri…
Optimal scheduling of entropy regulariser for continuous-time linear-quadratic reinforcement learning
Lukasz Szpruch, Tanut Treetanthiploet, Yufei Zhang
This work uses the entropy-regularised relaxed stochastic control perspective as a principled framework for designing reinforcement learning (RL) algorithms. Herein agent interacts…
Exploration-exploitation trade-off for continuous-time episodic reinforcement learning with linear-convex models
Lukasz Szpruch, Tanut Treetanthiploet, Yufei Zhang
We develop a probabilistic framework for analysing model-based reinforcement learning in the episodic setting. We then apply it to study finite-time horizon stochastic control prob…
Understanding Deep Architectures with Reasoning Layer
Xinshi Chen, Yufei Zhang, Christoph Reisinger +1
Recently, there has been a surge of interest in combining deep learning models with reasoning in order to handle more sophisticated learning tasks. In many cases, a reasoning task…