Probabilistic design of optimal sequential decision-making algorithms in learning and control
This survey is focused on certain sequential decision-making problems that involve
optimizing over probability functions. We discuss the relevance of these problems for …
optimizing over probability functions. We discuss the relevance of these problems for …
Intelligent computational techniques for physical object properties discovery, detection, and prediction: A comprehensive survey
The exploding usage of physical object properties has greatly facilitated real-time
applications such as robotics to perceive exactly as it appears in existence. Changes in the …
applications such as robotics to perceive exactly as it appears in existence. Changes in the …
CRAWLING: a crowdsourcing algorithm on wheels for smart parking
We present the principled design of CRAWLING: a CRowdsourcing Algorithm on WheeLs
for smart parkING. CRAWLING is an in-car service for the routing of connected cars …
for smart parkING. CRAWLING is an in-car service for the routing of connected cars …
Guaranteeing control requirements via reward shaping in reinforcement learning
In addressing control problems such as regulation and tracking through reinforcement
learning (RL), it is often required to guarantee that the acquired policy meets essential …
learning (RL), it is often required to guarantee that the acquired policy meets essential …
CT-DQN: Control-tutored deep reinforcement learning
One of the major challenges in Deep Reinforcement Learning for control is the need for
extensive training to learn the policy. Motivated by this, we present the design of the Control …
extensive training to learn the policy. Motivated by this, we present the design of the Control …
In vivo learning-based control of microbial populations density in bioreactors
SM Brancato, D Salzano, F De Lellis… - … Annual Learning for …, 2024 - proceedings.mlr.press
A key problem in using microorganisms as bio-factories is achieving and maintaining
cellular communities at the desired density and composition to efficiently convert their …
cellular communities at the desired density and composition to efficiently convert their …
Emergent Cooperative Strategies for Multi-Agent Shepherding via Reinforcement Learning
I Napolitano, A Lama, F De Lellis… - arXiv preprint arXiv …, 2024 - arxiv.org
We present a decentralized reinforcement learning (RL) approach to address the multi-
agent shepherding control problem, departing from the conventional assumption of cohesive …
agent shepherding control problem, departing from the conventional assumption of cohesive …