Reinforcement Learning Foundations Algorithms And Applications
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REINFORCEMENT LEARNING: FOUNDATIONS, ALGORITHMS AND APPLICATIONS
Author: Dr. Darío Salguero García
language: en
Publisher: Xoffencerpublication
Release Date: 2023-09-18
Reinforcement learning, sometimes known as RL, is a catchall word that refers to both a learning problem and a subfield in machine learning. In the context of a problem involving learning, this refers to the process of determining how to guide a computer toward an arbitrary numerical objective. The process of reinforcement learning may be seen in its usual application in the controller is provided with both the present state of the system under their control as well as the reward earned from the most recent transition. After that, the system will calculate an answer and then provide it to you. Because of this, the system goes through a state transition, and the process starts all over again. Figuring out how to have the most possible impact on the system in order to get the greatest possible advantage from it is the task at hand here. The gathering of data and the measurement of performance are two areas in which the learning obstacles are distinct. In this context, we make the assumption that the target system is, by its very nature, unpredictable. In addition, we make the assumption that the measures of state that are now accessible are detailed enough so that the controller does not need to speculate on how to get state information. The Markovian decision processes, often known as MDPs, provide a helpful framework for modeling issues that include these characteristics. MDPs are often "solved" via the use of dynamic programming, which, in practice, does nothing more than recast the initial problem as one involving the selection of an acceptable value function. Dynamic programming, on the other hand, is impractical in all but the most elementary of situations, namely those in which the MDP has a limited number of states and actions. The RL algorithms that we give here may be seen as a method that can be utilized to turn unfeasible dynamic programming into usable algorithms that can be used to real-world applications on a huge scale. The reason why RL algorithms are able to do this task is due to two key assumptions. The fundamental idea is to illustrate the dynamics of the control issue in a more concise way by utilizing samples. This is crucial for two reasons, which are as follows: To begin, it makes it easier to handle learning circumstances that include dynamics that are unknown.
Imbalanced Learning
The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. The first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications. Featuring contributions from experts in both academia and industry, Imbalanced Learning: Foundations, Algorithms, and Applications provides chapter coverage on: Foundations of Imbalanced Learning Imbalanced Datasets: From Sampling to Classifiers Ensemble Methods for Class Imbalance Learning Class Imbalance Learning Methods for Support Vector Machines Class Imbalance and Active Learning Nonstationary Stream Data Learning with Imbalanced Class Distribution Assessment Metrics for Imbalanced Learning Imbalanced Learning: Foundations, Algorithms, and Applications will help scientists and engineers learn how to tackle the problem of learning from imbalanced datasets, and gain insight into current developments in the field as well as future research directions.
Reinforcement Learning
Author: Richard S. Sutton
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Reinforcement learning is the learning of a mapping from situations to actions so as to maximize a scalar reward or reinforcement signal. The learner is not told which action to take, as in most forms of machine learning, but instead must discover which actions yield the highest reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation, and through that all subsequent rewards. These two characteristics -- trial-and-error search and delayed reward -- are the most important distinguishing features of reinforcement learning. Reinforcement learning is both a new and a very old topic in AI. The term appears to have been coined by Minsk (1961), and independently in control theory by Walz and Fu (1965). The earliest machine learning research now viewed as directly relevant was Samuel's (1959) checker player, which used temporal-difference learning to manage delayed reward much as it is used today. Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work. One could in fact consider all of reinforcement learning to be simply the reverse engineering of certain psychological learning processes (e.g. operant conditioning and secondary reinforcement). Reinforcement Learning is an edited volume of original research, comprising seven invited contributions by leading researchers.