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From Shortest Paths to Reinforcement Learning

experimentation and hands-on experience. The book assumes basic familiarity with probability and optimization, and is suitable to both practitioners and;

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From Shortest Paths to Reinforcement Learning

experimentation and hands-on experience. The book assumes basic familiarity with probability and optimization, and is suitable to both practitioners and;

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Reinforcement Learning for Finance

. It covers recent reinforcement learning algorithms from double deep-Q networks to twin-delayed deep deterministic policy gradients and;

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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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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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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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TensorFlow 2 Reinforcement Learning Cookbook

understanding of reinforcement learning concepts who want to build, train, and deploy their own reinforcement learning systems from scratch using;

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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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Reinforcement Learning

and then gain a clear picture of how they are inter-related. You'll then work with theories related to reinforcement learning and see the;

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Reinforcement Learning

discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. Reinforcement learning;

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Zai:Deep Reinforcement Learning i_p

intuitions that drive them * Applying reinforcement learning algorithms to real-world problems Audience You'll need intermediate;

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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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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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Algorithms Illuminated (Part 3)

Accessible, no-nonsense, and programming language-agnostic introduction to algorithms. Part 3 covers greedy algorithms (scheduling, minimum;

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Reinforcement Learning From Scratch

spectacular reinforcement learning algorithms work? With easy-to-understand explanations and clear examples in Java and Greenfoot, you can;

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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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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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Hands-On Reinforcement Learning for Games

Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and;

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Deep Reinforcement Learning

field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning.;

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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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Python Reinforcement Learning

learning algorithms but also the advanced deep reinforcement learning algorithms. The Learning Path starts with an introduction to RL followed by;

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Reinforcement and Systemic Machine Learning for Decision Making

area of computational intelligence. This book focuses on reinforcement and systemic learning to build a new learning paradigm, which makes;

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Learn Unity ML-Agents - Fundamentals of Unity Machine Learning

can be trained with machine learning methods through a simple-to-use Python API. This book takes you from the basics of Reinforcement and Q;

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Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context

computationally complex problems. Therefore, this work explores a hybrid quantum approach to policy gradient methods in Reinforcement Learning. It;

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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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