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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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Python Reinforcement Learning Projects

using TensorFlow for RL Use RL algorithms in Python and TensorFlow to solve CartPole balancing Create deep reinforcement learning algorithms to;

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Algorithms for Reinforcement Learning

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure;

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Algorithms for Reinforcement Learning

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure;

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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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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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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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Reinforcement Learning for Finance

library. Reinforcement Learning for Finance begins by describing methods for training neural networks. Next, it discusses CNN and RNN - two;

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TensorFlow 2 Reinforcement Learning Cookbook

Discover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learning Key;

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Zai:Deep Reinforcement Learning i_p

of its past decisions, and uses this information to optimize its behavior for maximum long-term return. Deep reinforcement learning famously;

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Reinforcement Learning Algorithms

is intended for reinforcement learning students and researchers with a firm grasp of linear algebra, statistics, and optimization. Nevertheless, all;

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Foundations of Deep Reinforcement Learning

The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL;

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Reinforcement Learning Algorithms with Python

for building self-learning agents Work with advanced Reinforcement Learning concepts and algorithms such as imitation learning and evolution;

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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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Reinforcement Learning

of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part;

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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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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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Hands-On Reinforcement Learning for Games

book will help you build a strong foundation in reinforcement learning for game development. Each chapter will assist you in implementing;

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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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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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Reinforcement Learning

Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from;

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The The Reinforcement Learning Workshop

policy-based methods with this practical guide Key Features Use TensorFlow to write reinforcement learning agents for performing;

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Neural Fuzzy Control Systems With Structure And Parameter Learning

applications, exact training data may be expensive or even impossible to get. To solve this reinforcement learning problem for real-world applications;

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Simulation-Based Optimization

special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite;

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MultiAgent Coordination

Coordination: A Reinforcement Learning Approach delivers a comprehensive, insightful, and unique treatment of the development of multi-robot;

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