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Transfer in Reinforcement Learning Domains

In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. The RL;

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Neural Information Processing

techniques across different domains. The third volume, LNCS 11303, is organized in topical sections on embedded learning, transfer learning;

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Reinforcement Learning

Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational;

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Python Reinforcement Learning Projects

Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful libraries Key Features Implement Q-learning;

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Transfer Of Learning

Educators and educational psychologists recognize transfer of learning as perhaps the most significant issue in all fields;

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Advanced Data Analytics Using Python

analysis, classifications, regression, and clustering Explore supervised learning, unsupervised learning, reinforcement learning, and transfer;

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Advanced Data Analytics Using Python

analysis, classifications, regression, and clustering Explore supervised learning, unsupervised learning, reinforcement learning, and transfer;

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Teaching Complex Cognitive Skills

Recently, traditional instructional design methodologies have been criticized that they may be ineffective in reaching transfer of learning;

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Reinforcement Learning

Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve;

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Reinforcement Learning for Cyber-Physical Systems

Reinforcement Learning for Cyber-Physical Systems: with Cybersecurity Case Studies was inspired by recent developments in the fields;

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Reinforcement Learning for Cyber-Physical Systems

Reinforcement Learning for Cyber-Physical Systems: with Cybersecurity Case Studies was inspired by recent developments in the fields;

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An Introduction to Machine Learning

models, and a much more detailed treatment of reinforcement learning. The book is written in an easy-to-understand manner with many examples and;

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Learning Learning

, generalization, and transfer. The final chapters (chapters 8-12) focus mostly on human forms of learning, such social learning and language learning;

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Reinforcement Learning for Finance

This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow;

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Python Reinforcement Learning

Apply modern reinforcement learning and deep reinforcement learning methods using Python and its powerful libraries Key Features Your entry;

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Applied Reinforcement Learning with Python

Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important;

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Deep Reinforcement Learning with Python

Deep reinforcement learning is a fast-growing discipline that is making a significant impact in fields of autonomous vehicles, robotics;

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Grokking Deep Reinforcement Learning

Written for developers with some understanding of deep learning algorithms. Experience with reinforcement learning is not required;

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Human-Robot Interaction Control Using Reinforcemen t Learning

, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control;

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Statistical Reinforcement Learning

Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic;

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Statistical Reinforcement Learning

Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic;

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Layered Learning in Multiagent Systems

reinforcement learning algorithm-team-partitioned, opaque-transition reinforcement learning (TPOT-RL)-designed for domains in which agents cannot;

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Motivated Reinforcement Learning

extend reinforcement learning to adaptive, multitask learning in complex, dynamic environments - the goal being to understand how machines can;

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Hands-On Reinforcement Learning with Python

A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python Key Features Your entry point into the;

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Supervised Reinforcement Learning

Supervised Reinforcement Learning is een boek van Karla Conn;

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Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

This book constitutes the refereed proceedings of the First MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019;

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Hands-On Neural Networks

demonstrate how you can implement advanced concepts including transfer learning, generative adversarial networks (GANs), autoencoders, and;

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