Efficient Reinforcement Learning Using Gaussian Processes

This book PDF is perfect for those who love Electronic computers. Computer science genre, written by Marc Peter Deisenroth and published by KIT Scientific Publishing which was released on 08 May 2024 with total hardcover pages 226. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Efficient Reinforcement Learning Using Gaussian Processes books below.

Efficient Reinforcement Learning Using Gaussian Processes
Efficient Reinforcement Learning Using Gaussian Processes
Author : Marc Peter Deisenroth
File Size : 48,5 Mb
Publisher : KIT Scientific Publishing
Language : English
Release Date : 08 May 2024
ISBN : 9783866445697
Pages : 226 pages
Get Book

Efficient Reinforcement Learning Using Gaussian Processes by Marc Peter Deisenroth Book PDF Summary

This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model uncertainties consistently into account during long-term planning to reduce model bias. Second, we propose principled algorithms for robust filtering and smoothing in GP dynamic systems.

Efficient Reinforcement Learning Using Gaussian Processes

Efficient Reinforcement Learning Using Gaussian Processes by Marc Peter Deisenroth

This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model uncertainties consistently into account during long-term planning

Get Book
Bayesian Reasoning and Gaussian Processes for Machine Learning Applications

Bayesian Reasoning and Gaussian Processes for Machine Learning Applications by Hemachandran K,Shubham Tayal,Preetha Mary George,Parveen Singla,Utku Kose

This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian

Get Book
Robotics

Robotics by Nicholas Roy,Paul Newman,Siddhartha Srinivasa

Papers from a flagship conference reflect the latest developments in the field, including work in such rapidly advancing areas as human-robot interaction and formal methods. Robotics: Science and Systems VIII spans a wide spectrum of robotics, bringing together contributions from researchers working on the mathematical foundations of robotics, robotics applications,

Get Book
Modelling and Control of Dynamic Systems Using Gaussian Process Models

Modelling and Control of Dynamic Systems Using Gaussian Process Models by Juš Kocijan

This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (

Get Book
Quantitative Evaluation of Systems

Quantitative Evaluation of Systems by Marco Gribaudo,David N. Jansen,Anne Remke

This book constitutes the proceedings of the 17th International Conference on Quantitative Evaluation Systems, QEST 2020, held in Vienna, Austria, in August/September 2020. The 12 full papers presented together with 7 short papers were carefully reviewed and selected from 42 submissions. The papers cover topics such as classic measures involving performance and reliability, quantification

Get Book
Reinforcement Learning for Optimal Feedback Control

Reinforcement Learning for Optimal Feedback Control by Rushikesh Kamalapurkar,Patrick Walters,Joel Rosenfeld,Warren Dixon

Reinforcement Learning for Optimal Feedback Control develops model-based and data-driven reinforcement learning methods for solving optimal control problems in nonlinear deterministic dynamical systems. In order to achieve learning under uncertainty, data-driven methods for identifying system models in real-time are also developed. The book illustrates the advantages gained from the use

Get Book
Innovations in Intelligent Machines 5

Innovations in Intelligent Machines 5 by Valentina Emilia Balas,Petia Koprinkova-Hristova,Lakhmi C. Jain

This research monograph presents selected areas of applications in the field of control systems engineering using computational intelligence methodologies. A number of applications and case studies are introduced. These methodologies are increasing used in many applications of our daily lives. Approaches include, fuzzy-neural multi model for decentralized identification, model predictive

Get Book
Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases by Hendrik Blockeel,Kristian Kersting,Siegfried Nijssen,Filip Železný

This three-volume set LNAI 8188, 8189 and 8190 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2013, held in Prague, Czech Republic, in September 2013. The 111 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 447 submissions. The papers are organized

Get Book