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Learning to Play

of intelligence and games, the book is organized into chapters on reinforcement learning, heuristic planning, adaptive sampling, function approximation;

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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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Motivated Reinforcement Learning

learning agents can be used in computer games for the design of non-player characters that can adapt their behaviour in response to unexpected;

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Python Reinforcement Learning Projects

even build other neural networks. By the end of this book, you will have hands-on experience with eight reinforcement learning projects, each;

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Multi-Agent Machine Learning

methods, and stochastic approximation. Chapter 2 covers single agent reinforcement learning. Topics include learning value functions, Markov games;

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Hands-On Deep Learning for Games

Understand the core concepts of deep learning and deep reinforcement learning by applying them to develop games Key Features Apply the;

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Reinforcement Learning

articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical;

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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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Deep Reinforcement Learning Hands-On

Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross;

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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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Deep Reinforcement Learning Hands-On

Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning;

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Learn Unity ML-Agents - Fundamentals of Unity Machine Learning

cooperative and competitive multi-agent ecosystem. What you will learn Develop Reinforcement and Deep Reinforcement Learning for games. Understand;

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Reinforcement Learning From Scratch

programming concepts. The result is an accessible introduction into machine learning that concentrates on reinforcement learning. Taking the;

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PyTorch Deep Learning Hands-On

learning models from prototyping to production Book DescriptionPyTorch Deep Learning Hands-On is a book for engineers who want a fast-paced guide;

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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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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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Hands-On Artificial Intelligence for Beginners

, and GNNs), as well as reinforcement learning methods. In the concluding chapters, you'll learn how to implement these methods for a variety;

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Reinforcement Learning and Approximate Dynamic Programming for Feedback Control

Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and;

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Python Reinforcement Learning

Learning Path includes content from the following Packt products: Hands-On Reinforcement Learning with Python by Sudharsan Ravichandiran Python;

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Making Multiplication Easy

Make learning the multiplication tables easy and entertaining for kids of all learning styles! Packed with teacher-tested games, rhymes;

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Distance Learning for Elementary STEM

for games and reinforcement. Hands-on activities and engineering design challenges. Connections to various content areas as well as;

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Modern Plumbing

, and a Lab Workbook offers additional hands-on labs and learning reinforcement.;

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Introduction to Artificial Intelligence

, machine learning, neural networks and reinforcement learning. Topics and features: presents an application-focused and hands-on approach to;

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Hands-On Intelligent Agents with OpenAI Gym

algorithms using deep reinforcement learning starting from the implementation of the building blocks for configuring, training, logging, visualizing;

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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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Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles

engineered systems. ADP is a reinforcement machine learning technique that is motivated by learning mechanisms in biological and animal systems. It;

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Markov Decision Processes in Artificial Intelligence

Reinforcement Learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in Artificial;

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