4 citations · 9 across the 9 of their papers we have counts for
4 papers · 1 filter
Semismooth Newton Methods for Risk-Averse Markov Decision Processes
Matilde Gargiani, Francesco Micheli, Anastasios Tsiamis +1
Inspired by semismooth Newton methods, we propose a general framework for designing solution methods with convergence guarantees for risk-averse Markov decision processes. Our appr…
Inexact GMRES Policy Iteration for Large-Scale Markov Decision Processes
Matilde Gargiani, Dominic Liao-McPherson, Andrea Zanelli +1
Policy iteration enjoys a local quadratic rate of contraction, but its iterations are computationally expensive for Markov decision processes (MDPs) with a large number of states.…
Parallel and Flexible Dynamic Programming via the Randomized Mini-Batch Operator
Matilde Gargiani, Andrea Martinelli, Max Ruts Martinez +1
The Bellman operator constitutes the foundation of dynamic programming (DP). An alternative is presented by the Gauss-Seidel operator, whose evaluation, differently from that of th…
On the Synthesis of Bellman Inequalities for Data-Driven Optimal Control
Andrea Martinelli, Matilde Gargiani, John Lygeros
In the context of the linear programming (LP) approach to data-driven control, one assumes that the dynamical system is unknown but can be observed indirectly through data on its e…