12 citations · 16 across the 5 of their papers we have counts for
7 papers · 1 filter
On the convex formulations of robust Markov decision processes
Julien Grand-Clément, Marek Petrik
Robust Markov decision processes (MDPs) are used for applications of dynamic optimization in uncertain environments and have been studied extensively. Many of the main properties a…
Solving optimization problems with Blackwell approachability
Julien Grand-Clément, Christian Kroer
We introduce the Conic Blackwell Algorithm (CBA) regret minimizer, a new parameter- and scale-free regret minimizer for general convex sets. CBA is based on Blackwell a…
From Convex Optimization to MDPs: A Review of First-Order, Second-Order and Quasi-Newton Methods for MDPs
Julien Grand-Clément
In this paper we present a review of the connections between classical algorithms for solving Markov Decision Processes (MDPs) and classical gradient-based algorithms in convex opt…
First-Order Methods for Wasserstein Distributionally Robust MDP
Julien Grand-Clément, Christian Kroer
Markov decision processes (MDPs) are known to be sensitive to parameter specification. Distributionally robust MDPs alleviate this issue by allowing for \emph{ambiguity sets} which…
Scalable First-Order Methods for Robust MDPs
Julien Grand-Clément, Christian Kroer
Robust Markov Decision Processes (MDPs) are a powerful framework for modeling sequential decision-making problems with model uncertainty. This paper proposes the first first-order…
A First-Order Approach To Accelerated Value Iteration
Vineet Goyal, Julien Grand-Clement
Markov decision processes (MDPs) are used to model stochastic systems in many applications. Several efficient algorithms to compute optimal policies have been studied in the litera…