12 citations · 32 across the 21 of their papers we have counts for
6 papers · 1 filter
Value iteration with stopping criterion: finite iterations, stability, and near-optimality guarantees
Mathieu Granzotto, Romain Postoyan, Dragan Nešić +2
Value iteration (VI) is a cornerstone of dynamic programming that allows computing near-optimal feedback laws for general plant dynamics and cost functions. In practice, however, i…
An optimistic planning algorithm for switched discrete-time LQR
Mathieu Granzotto, Romain Postoyan, Dragan Nešić +2
We introduce TROOP, a tree-based Riccati optimistic online planner, that is designed to generate near-optimal control laws for discrete-time switched linear systems with switched q…
Near-optimal control of nonlinear systems with hybrid inputs and dwell-time constraints
Ioana Lal, Constantin Morarescu, Jamal Daafouz +1
We propose two new optimistic planning algorithms for nonlinear hybrid-input systems, in which the input has both a continuous and a discrete component, and the discrete component…
Exploiting homogeneity for the optimal control of discrete-time systems: application to value iteration
Mathieu Granzotto, Romain Postoyan, Lucian Buşoniu +2
To investigate solutions of (near-)optimal control problems, we extend and exploit a notion of homogeneity recently proposed in the literature for discrete-time systems. Assuming t…
When to stop value iteration: stability and near-optimality versus computation
Mathieu Granzotto, Romain Postoyan, Dragan Nešić +2
Value iteration (VI) is a ubiquitous algorithm for optimal control, planning, and reinforcement learning schemes. Under the right assumptions, VI is a vital tool to generate inputs…
Optimistic planning for the near-optimal control of nonlinear switched discrete-time systems with stability guarantees
Mathieu Granzotto, Romain Postoyan, Lucian Buşoniu +2
Originating in the artificial intelligence literature, optimistic planning (OP) is an algorithm that generates near-optimal control inputs for generic nonlinear discrete-time syste…