Search Results for author:
Found 16 papers, 4 papers with code
In vivo learning-based control of microbial populations density in bioreactors
Our work showcases the viability of learning-based strategies for the control of cellular density in bioreactors, making a step forward toward their use for the control of the composition of microbial consortia.
Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learning
In addressing control problems such as regulation and tracking through reinforcement learning, it is often required to guarantee that the acquired policy meets essential performance and stability criteria such as a desired settling time and steady-state error prior to deployment.
Positive Competitive Networks for Sparse Reconstruction
Our analysis leverages contraction theory to characterize the behavior of a family of firing-rate competitive networks for sparse reconstruction with and without non-negativity constraints.
On Convex Data-Driven Inverse Optimal Control for Nonlinear, Non-stationary and Stochastic Systems
This paper is concerned with a finite-horizon inverse control problem, which has the goal of inferring, from observations, the possibly non-convex and non-stationary cost driving the actions of an agent.
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.
A Multiplex Approach Against Disturbance Propagation in Nonlinear Networks with Delays
We consider both leaderless and leader-follower, possibly nonlinear, networks affected by time-varying communication delays.
External control of a genetic toggle switch via Reinforcement Learning
We investigate the problem of using a learning-based strategy to stabilize a synthetic toggle switch via an external control approach.
On the design of scalable networks rejecting first order disturbances
Specifically, we propose the use of a multiplex architecture to design distributed control protocols to reject polynomial disturbances up to ramps and guarantee a scalability property that prohibits the amplification of residual disturbances.
Discrete fully probabilistic design: towards a control pipeline for the synthesis of policies from examples
Contrary to other approaches, the pipeline we present: (i) does not need the constraints to be fulfilled in the possibly noisy example data; (ii) enables control synthesis even when the data are collected from an example system that is different from the one under control.
Intermittent non-pharmaceutical strategies to mitigate the COVID-19 epidemic in a network model of Italy via constrained optimization
This paper is concerned with the design of intermittent non-pharmaceutical strategies to mitigate the spread of the COVID-19 epidemic exploiting network epidemiological models.
Tutoring Reinforcement Learning via Feedback Control
We introduce a control-tutored reinforcement learning (CTRL) algorithm.
Scalability in nonlinear network systems affected by delays and disturbances
This paper is concerned with the study of scalability in nonlinear heterogeneous networks affected by communication delays and disturbances.
Intermittent yet coordinated regional strategies can alleviate the COVID-19 epidemic: a network model of the Italian case
Using the model, we confirm the effectiveness at the regional level of the national lockdown strategy implemented so far by the Italian government to mitigate the spread of the disease and show its efficacy at the regional level.
Physics and Society Populations and Evolution 93C10, 92D30, 92D25 J.2
Control-Tutored Reinforcement Learning
We introduce a control-tutored reinforcement learning (CTRL) algorithm.
Control-Tutored Reinforcement Learning: an application to the Herding Problem
In this extended abstract we introduce a novel control-tutored Q-learning approach (CTQL) as part of the ongoing effort in developing model-based and safe RL for continuous state spaces.
Driving Reinforcement Learning with Models
In this paper we propose a new approach to complement reinforcement learning (RL) with model-based control (in particular, Model Predictive Control - MPC).
