Search Results for author:
Found 12 papers, 1 papers with code
Exploiting Estimation Bias in Deep Double Q-Learning for Actor-Critic Methods
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.
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification
First, instead of directly modeling the inverse dynamics components, we model as GPs the kinetic and potential energy of the system.
Learning Control from Raw Position Measurements
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.
Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models
We tested the two strategies on a simulated manipulator with seven degrees of freedom, also varying the GP kernel choice.
Model-Based Policy Search Using Monte Carlo Gradient Estimation with Real Systems Application
The algorithm relies on Gaussian Processes (GPs) to model the system dynamics and on a Monte Carlo approach to estimate the policy gradient.
Model-based Policy Search for Partially Measurable Systems
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.
Extrapolation-based Prediction-Correction Methods for Time-varying Convex Optimization
In this paper, we focus on the solution of online optimization problems that arise often in signal processing and machine learning, in which we have access to streaming sources of data.
Smart Grid State Estimation with PMUs Time Synchronization Errors
We consider the problem of PMU-based state estimation combining information coming from ubiquitous power demand time series and only a limited number of PMUs.
Optimization and Control Systems and Control Systems and Control
A novel Multiplicative Polynomial Kernel for Volterra series identification
Volterra series are especially useful for nonlinear system identification, also thanks to their capability to approximate a broad range of input-output maps.
A data-efficient geometrically inspired polynomial kernel for robot inverse dynamics
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).
Efficient Spatio-Temporal Gaussian Regression via Kalman Filtering
In this work we study the non-parametric reconstruction of spatio-temporal dynamical Gaussian processes (GPs) via GP regression from sparse and noisy data.
Multi-agents adaptive estimation and coverage control using Gaussian regression
We consider a scenario where the aim of a group of agents is to perform the optimal coverage of a region according to a sensory function.
