no code implementations • 14 Feb 2024 • Alberto Sinigaglia, Niccolò Turcato, Alberto Dalla Libera, Ruggero Carli, Gian Antonio Susto
This paper introduces innovative methods in Reinforcement Learning (RL), focusing on addressing and exploiting estimation biases in Actor-Critic methods for continuous control tasks, using Deep Double Q-Learning.
no code implementations • 10 Oct 2023 • Giulio Giacomuzzo, Alberto Dalla Libera, Diego Romeres, Ruggero Carli
First, instead of directly modeling the inverse dynamics components, we model as GPs the kinetic and potential energy of the system.
no code implementations • 29 May 2023 • Enrico Picotti, Enrico Mion, Alberto Dalla Libera, Josip Pavlovic, Andrea Censi, Emilio Frazzoli, Alessandro Beghi, Mattia Bruschetta
Lately, Nonlinear Model Predictive Control (NMPC)has been successfully applied to (semi-) autonomous driving problems and has proven to be a very promising technique.
no code implementations • 30 Jan 2023 • Fabio Amadio, Alberto Dalla Libera, Daniel Nikovski, Ruggero Carli, Diego Romeres
We propose a Model-Based Reinforcement Learning (MBRL) algorithm named VF-MC-PILCO, specifically designed for application to mechanical systems where velocities cannot be directly measured.
no code implementations • 26 Apr 2021 • Alberto Dalla Libera, Fabio Amadio, Daniel Nikovski, Ruggero Carli, Diego Romeres
We tested the two strategies on a simulated manipulator with seven degrees of freedom, also varying the GP kernel choice.
no code implementations • 28 Jan 2021 • Fabio Amadio, Alberto Dalla Libera, Riccardo Antonello, Daniel Nikovski, Ruggero Carli, Diego Romeres
The algorithm relies on Gaussian Processes (GPs) to model the system dynamics and on a Monte Carlo approach to estimate the policy gradient.
no code implementations • 21 Jan 2021 • Fabio Amadio, Alberto Dalla Libera, Ruggero Carli, Daniel Nikovski, Diego Romeres
In this paper, we propose a Model-Based Reinforcement Learning (MBRL) algorithm for Partially Measurable Systems (PMS), i. e., systems where the state can not be directly measured, but must be estimated through proper state observers.
no code implementations • 25 Feb 2020 • Alberto Dalla Libera, Diego Romeres, Devesh K. Jha, Bill Yerazunis, Daniel Nikovski
In this paper, we propose a derivative-free model learning framework for Reinforcement Learning (RL) algorithms based on Gaussian Process Regression (GPR).
no code implementations • 20 May 2019 • Alberto Dalla Libera, Ruggero Carli, Gianluigi Pillonetto
Volterra series are especially useful for nonlinear system identification, also thanks to their capability to approximate a broad range of input-output maps.
no code implementations • 30 Apr 2019 • Alberto Dalla Libera, Ruggero Carli
Driven by the fact that the inverse dynamics can be described as a polynomial function on a suitable input space, we propose the use of a novel kernel, called Geometrically Inspired Polynomial Kernel (GIP).
no code implementations • 13 Sep 2018 • Diego Romeres, Devesh Jha, Alberto Dalla Libera, William Yerazunis, Daniel Nikovski
We propose the system presented in the paper as a benchmark problem for reinforcement and robot learning, for its interesting and challenging dynamics and its relative ease of reproducibility.