56 citations · 200 across the 32 of their papers we have counts for
5 papers · 1 filter
Foundations of Reinforcement Learning and Control:Connections and New Perspectives
Claire Vernade, Onno Eberhard, Martha White +4
Reinforcement learning and control theory are two adjacent scientific fields that focus on optimizing the controller of unknown dynamical systems using feedback. While both fields…
To Retain or to Adapt? Generalizing Continual Learning
Giulia Lanzillotta, Mandana Samiei, Doina Precup +2
The Continual Learning (CL) literature has long been driven by the goal of mitigating catastrophic forgetting. This objective rests on an often unstated assumption: that a lifelong…
Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation
Ziyad Sheebaelhamd, Luca Viano, Volkan Cevher +1
This work investigates multi-objective imitation learning: the problem of recovering policies that lie on the Pareto front given demonstrations from multiple Pareto-optimal experts…
Tight Sample Complexity Bounds for Entropic Best Policy Identification
Amer Essakine, Claire Vernade
We study best-policy identification for finite-horizon risk-sensitive reinforcement learning under the entropic risk measure. Recent work established a constant gap in the exponent…
Commit to the Bit: Reactive Reinforcement Learning Done Right
Onno Eberhard, Claire Vernade, Michael Muehlebach
Reinforcement learning algorithms are commonly analyzed (and designed) under the Markov assumption. This is unrealistic, as most environments encountered in practice are either par…