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Found 11 papers, 0 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.
A Learning-based Nonlinear Model Predictive Controller for a Real Go-Kart based on Black-box Dynamics Modeling through Gaussian Processes
Lately, Nonlinear Model Predictive Control (NMPC)has been successfully applied to (semi-) autonomous driving problems and has proven to be a very promising technique.
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.
Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements
In this paper, we propose a derivative-free model learning framework for Reinforcement Learning (RL) algorithms based on Gaussian Process Regression (GPR).
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).
Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze
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.
