4 papers
Advantage-Guided Diffusion for Model-Based Reinforcement Learning
Daniele Foffano, Arvid Eriksson, David Broman +2
Model-based reinforcement learning (MBRL) with autoregressive world models suffers from compounding errors, whereas diffusion world models mitigate this by generating trajectory se…
Receding-Horizon Control via Drifting Models
Daniele Foffano, Alessio Russo, Alexandre Proutiere
We study the problem of trajectory optimization in settings where the system dynamics are unknown and it is not possible to simulate trajectories through a surrogate model. When an…
Adversarial Diffusion for Robust Reinforcement Learning
Daniele Foffano, Alessio Russo, Alexandre Proutiere
Robustness to modeling errors and uncertainties remains a central challenge in reinforcement learning (RL). In this work, we address this challenge by leveraging diffusion models t…
Conformal Off-Policy Evaluation in Markov Decision Processes
Daniele Foffano, Alessio Russo, Alexandre Proutiere
Reinforcement Learning aims at identifying and evaluating efficient control policies from data. In many real-world applications, the learner is not allowed to experiment and cannot…