28 citations · 53 across the 8 of their papers we have counts for
8 papers
Safe model-based Reinforcement Learning via Model Predictive Control and Control Barrier Functions
Kerim Dzhumageldyev, Filippo Airaldi, Azita Dabiri
Optimal control strategies are often combined with safety certificates to ensure both performance and safety in safety-critical systems. A prominent example is combining Model Pred…
Second-Order MPC-Based Distributed Q-Learning
Samuel Mallick, Filippo Airaldi, Azita Dabiri +1
The state of the art for model predictive control (MPC)-based distributed Q-learning is limited to first-order gradient updates of the MPC parameterization. In general, using secon…
Probabilistically safe and efficient model-based reinforcement learning
Filippo Airaldi, Bart De Schutter, Azita Dabiri
This paper proposes tackling safety-critical stochastic Reinforcement Learning (RL) tasks with a sample-based, model-based approach. At the core of the method lies a Model Predicti…
Nonmyopic Global Optimisation via Approximate Dynamic Programming
Filippo Airaldi, Bart De Schutter, Azita Dabiri
Global optimisation to optimise expensive-to-evaluate black-box functions without gradient information. Bayesian optimisation, one of the most well-known techniques, typically empl…
Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control
Samuel Mallick, Filippo Airaldi, Azita Dabiri +2
Greenhouse climate control is concerned with maximizing performance in terms of crop yield and resource efficiency. One promising approach is model predictive control (MPC), which…
Multi-Agent Reinforcement Learning via Distributed MPC as a Function Approximator
Samuel Mallick, Filippo Airaldi, Azita Dabiri +1
This paper presents a novel approach to multi-agent reinforcement learning (RL) for linear systems with convex polytopic constraints. Existing work on RL has demonstrated the use o…
Reinforcement Learning with Model Predictive Control for Highway Ramp Metering
Filippo Airaldi, Bart De Schutter, Azita Dabiri
In the backdrop of an increasingly pressing need for effective urban and highway transportation systems, this work explores the synergy between model-based and learning-based strat…
Learning safety in model-based Reinforcement Learning using MPC and Gaussian Processes
Filippo Airaldi, Bart De Schutter, Azita Dabiri
We propose a method to encourage safety in Model Predictive Control (MPC)-based Reinforcement Learning (RL) via Gaussian Process (GP) regression. This framework consists of 1) a pa…