Efficient Reinforcement Learning Using Gaussian Processes - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Efficient Reinforcement Learning Using Gaussian Processes write by Marc Peter Deisenroth. This book was released on 2010. Efficient Reinforcement Learning Using Gaussian Processes available in PDF, EPUB and Kindle. 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.
Gaussian Processes in Reinforcement Learning: Stability Analysis and Efficient Value Propagation
Gaussian Processes in Reinforcement Learning: Stability Analysis and Efficient Value Propagation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Gaussian Processes in Reinforcement Learning: Stability Analysis and Efficient Value Propagation write by Julia Vinogradska. This book was released on 2018. Gaussian Processes in Reinforcement Learning: Stability Analysis and Efficient Value Propagation available in PDF, EPUB and Kindle.
Gaussian Processes for Machine Learning
Gaussian Processes for Machine Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Gaussian Processes for Machine Learning write by Carl Edward Rasmussen. This book was released on 2005-11-23. Gaussian Processes for Machine Learning available in PDF, EPUB and Kindle. A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
Efficient Reinforcement Learning with Bayesian Optimization
Efficient Reinforcement Learning with Bayesian Optimization - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Efficient Reinforcement Learning with Bayesian Optimization write by Danyan Ganjali. This book was released on 2016. Efficient Reinforcement Learning with Bayesian Optimization available in PDF, EPUB and Kindle. A probabilistic reinforcement learning algorithm is presented for finding control policies in continuous state and action spaces without a prior knowledge of the dynamics. The objective of this algorithm is to learn from minimal amount of interaction with the environment in order to maximize a notion of reward, i.e. a numerical measure of the quality of the resulting state trajectories. Experience from the interactions are used to construct a set of probabilistic Gaussian process (GP) models that predict the resulting state trajectories and the reward from executing a policy on the system. These predictions are used with a technique known as Bayesian optimization to search for policies that promise higher rewards. As more experience is gathered, predictions are made with more confidence and the search for better policies relies less on new interactions with the environment.The computational demand of a GP makes it eventually impractical to use as the number of observations from interacting with the environment increase. Moreover, using a single GP to model different regions that may exhibit disparate behaviors can produce unsatisfactory representations and predictions. One way of mitigating these issues is by partitioning the observation points into different regions each represented by a local GP. With the sequential arrival of the observation points from new experiences, it is necessary to have an adaptive clustering method that can partition the data into an appropriate number of regions. This led to the development of EM + algorithm presented in the second part of this work, which is an extension to the Expectation Maximization (EM) for the Gaussian mixture models, that assumes no prior knowledge of the number of components.Lastly, an application of the EM+ algorithm to filtering problems is presented. We propose a filtering algorithm that combines the advantages of the well-known particle filter and the mixture of Gaussian filter, while avoiding their issues.
Algorithms for Reinforcement Learning
Algorithms for Reinforcement Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Algorithms for Reinforcement Learning write by Csaba Grossi. This book was released on 2022-05-31. Algorithms for Reinforcement Learning available in PDF, EPUB and Kindle. Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration
