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The The Reinforcement Learning Workshop

method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, you'll be equipped with the knowledge and skills needed to;

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Artificial Intelligence. IJCAI 2019 International Workshops

Reinforcement Learning. First International Workshop on Bringing Semantic Knowledge into Vision and Text Understanding. EASyHAT 2019: First;

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Recent Advances in Reinforcement Learning

symbolic levels. As a participant in this 8th European Workshop on Reinforcement Learning, I was struck by both the quality and quantity of the;

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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 Finance

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

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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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Grokking Deep Reinforcement Learning

reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and;

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Reinforcement Learning

, reinforcement learning has progressed tremendously in the past decade. The main goal of this book is to present an up-to-date series of survey;

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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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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

of statistical reinforcement learning from the modern machine learning viewpoint. It covers various types of RL approaches, including model-based and;

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Statistical Reinforcement Learning

of statistical reinforcement learning from the modern machine learning viewpoint. It covers various types of RL approaches, including model-based and;

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Deep Reinforcement Learning with Python

deep reinforcement learning implementation using popular deep learning frameworks such as TensorFlow and PyTorch. In the end, you'll;

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Machine Learning in Medical Imaging

This book constitutes the proceedings of the 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019, held;

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Reinforcement Learning

Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their;

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Algorithms for Reinforcement Learning

of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as;

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Algorithms for Reinforcement Learning

of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as;

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TensorFlow 2 Reinforcement Learning Cookbook

reinforcement learning algorithms such as Q-learning, SARSA, and the actor-critic method Customize and build RL-based applications for performing real;

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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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Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context

This book explores the combination of Reinforcement Learning and Quantum Computing in the light of complex attacker-defender scenarios;

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Zai:Deep Reinforcement Learning i_p

cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along;

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Reinforcement and Systemic Machine Learning for Decision Making

Reinforcement and Systemic Machine Learning for Decision Making explores a newer and growing avenue of machine learning algorithm in the;

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Advances in Hybridization of Intelligent Methods

, machine learning, reinforcement learning, deep learning, agent-based approaches, knowledge-based system and image processing. The book includes;

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Reinforcement Learning

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas;

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Reinforcement Learning Algorithms

on reinforcement learning held by Prof. Jan Peters in the winter semester 2018/2019 at Technische Universitat Darmstadt. The book;

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Handbook of Reinforcement Learning and Control

This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory;

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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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