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Reinforcement Learning and Dynamic Programming Using Function Approximators

economics, medicine, and artificial intelligence. Reinforcement Learning and Dynamic Programming Using Function Approximators provides a;

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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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Deep Reinforcement Learning with Python

foundation of deep reinforcement learning. Next, you'll study model-free learning followed by function approximation using neural networks and;

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The The Reinforcement Learning Workshop

Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and;

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Hands-On Reinforcement Learning with Python

will learn Understand the basics of reinforcement learning methods, algorithms, and elements Train an agent to walk using OpenAI Gym and;

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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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Simulation-Based Optimization

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and;

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Handbook of Reinforcement Learning and Control

when using learning and adaptation methods to solve academic and industrial problems, such as optimization in dynamic environments with single;

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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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Pattern Classification

classifiers through extensive performance evaluation of real data sets. It thus offers new learning paradigms for analyzing neural networks and fuzzy;

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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

well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number;

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Algorithms for Reinforcement Learning

well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number;

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Abstraction in Reinforcement Learning

Reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic;

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Hands-On Deep Learning with R

with using reinforcement learning and deep reinforcement learning to solve various real-world problems. By the end of this deep learning book;

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From Shortest Paths to Reinforcement Learning

Dynamic programming (DP) has a relevant history as a powerful and flexible optimization principle, but has a bad reputation as a;

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From Shortest Paths to Reinforcement Learning

Dynamic programming (DP) has a relevant history as a powerful and flexible optimization principle, but has a bad reputation as a;

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Practical MATLAB Deep Learning

What You Will Learn Explore deep learning using MATLAB and compare it to algorithms Write a deep learning function in MATLAB and train it;

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Practical MATLAB Deep Learning

provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. In this book, yoüll see how these toolboxes provide the;

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Human-Robot Interaction Control Using Reinforcemen t Learning

-free human-robot interaction control and human-in-the-loop control using Euler angles Practical discussions of reinforcement learning for;

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Partially Observed Markov Decision Proce

of stochastic dynamic programming, lattice programming and reinforcement learning for POMDPs. Questions addressed in the book include: when does a POMDP;

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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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Agile Visualization with Pharo

visualization using the Pharo programming language Chart, plot, and curve using Grapher Build and draw graphs using Mondrian Implement reinforcement;

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Reinforcement Learning Algorithms with Python

interface Discover the model-based reinforcement learning paradigm Solve the Frozen Lake problem with dynamic programming Explore Q-learning and;

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Reinforcement Learning

Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning Dive deep into a;

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TensorFlow 2 Reinforcement Learning Cookbook

reinforcement learning agents to the cloud and build cross-platform apps using TensorFlow 2.x. By the end of this TensorFlow book, you'll have;

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Reinforcement Learning

learning and AI Work with Open AI Gym, Open AI, and Python Harness reinforcement learning with TensorFlow and Keras using Python Who This Book;

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