12 citations · 14 across the 5 of their papers we have counts for
7 papers
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…
ObserveNet Control: A Vision-Dynamics Learning Approach to Predictive Control in Autonomous Vehicles
Cosmin Ginerica, Mihai Zaha, Florin Gogianu +3
A key component in autonomous driving is the ability of the self-driving car to understand, track and predict the dynamics of the surrounding environment. Although there is signifi…
Spectral Normalisation for Deep Reinforcement Learning: an Optimisation Perspective
Florin Gogianu, Tudor Berariu, Mihaela Rosca +3
Most of the recent deep reinforcement learning advances take an RL-centric perspective and focus on refinements of the training objective. We diverge from this view and show we can…
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…
Learning control for transmission and navigation with a mobile robot under unknown communication rates
L. Busoniu, V. S. Varma, J. Loheac +4
In tasks such as surveying or monitoring remote regions, an autonomous robot must move while transmitting data over a wireless network with unknown, position-dependent transmission…
Space-time budget allocation policy design for viral marketing
I. C. Morarescu, V. S. Varma, L. Busoniu +1
We address formally the problem of opinion dynamics when the agents of a social network (e.g., consumers) are not only influenced by their neighbors but also by an external influen…