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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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The Power of Reinforcement

and defends the use of reinforcement principles against a wide variety of attacks. Countering the myths, criticisms, and misrepresentations;

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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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Biological Determinants of Reinforcement

This seventh volume, divided into four parts, addresses the biological determinates of reinforcement and memory. Covers topics;

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Reinforcement of Timber Elements in Existing Structures

reinforcement of timber elements, joints and structures. It consists of two parts: part I examines state-of-the-art information on reinforcement;

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Human-Robot Interaction Control Using Reinforcemen t Learning

A comprehensive exploration of the control schemes of human-robot interactions In Human-Robot Interaction Control Using Reinforcement;

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Reinforcement of Rubber

This book presents the most recent description of rubber reinforcement, focusing on the network-like structure formation of nanofiller;

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Reinforcement of Rubber

This book presents the most recent description of rubber reinforcement, focusing on the network-like structure formation of nanofiller;

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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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Rock Reinforcement and Rock Support

conditions associated with them must be understood so that correct stabilisation of the structure through rock reinforcement and/or rock support can;

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Rock Reinforcement and Rock Support

conditions associated with them must be understood so that correct stabilisation of the structure through rock reinforcement and/or rock support can;

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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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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

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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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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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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New Horizons in Earth Reinforcement

earthquakes, flooding or tsunamis. NEW HORIZONS in EARTH REINFORCEMENT contains contributions from the 5th International Symposium on Earth;

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New Horizons in Earth Reinforcement

earthquakes, flooding or tsunamis. NEW HORIZONS in EARTH REINFORCEMENT contains contributions from the 5th International Symposium on Earth;

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Training Reinforcement

A proven framework to fill the gap between knowing and doing Training Reinforcement offers expert guidance for more effective;

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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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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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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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Minimum Reinforcement in Concrete Members

positive responses. The central topic discussed by the committee was that of the minimum reinforcement in concrete members. The minimum;

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