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    Hands-On Reinforcement Learning with Python

    Posted By: AlenMiler
    Hands-On Reinforcement Learning with Python

    Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow by Sudharsan Ravichandiran
    English | 28 Jun. 2018 | ISBN: 1788836529 | 318 Pages | EPUB | 8.76 MB

    A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python

    Key Features
    Your entry point into the world of artificial intelligence using the power of Python
    An example-rich guide to master various RL and DRL algorithms
    Explore various state-of-the-art architectures along with math

    Book Description
    Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.

    The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning.

    By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.

    What you will learn
    Understand the basics of reinforcement learning methods, algorithms, and elements
    Train an agent to walk using OpenAI Gym and Tensorflow
    Understand the Markov Decision Process, Bellman's optimality, and TD learning
    Solve multi-armed-bandit problems using various algorithms
    Master deep learning algorithms, such as RNN, LSTM, and CNN with applications
    Build intelligent agents using the DRQN algorithm to play the Doom game
    Teach agents to play the Lunar Lander game using DDPG
    Train an agent to win a car racing game using dueling DQN

    Who This Book Is For
    If you're a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.

    Table of Contents
    Introduction to Reinforcement Learning
    Getting started with OpenAI and Tensorflow
    Markov Decision process and Dynamic Programming
    Gaming with Monte Carlo Tree Search
    Temporal Difference Learning
    Multi-Armed Bandit Problem
    Deep Learning Fundamentals
    Deep Learning and Reinforcement
    Playing Doom With Deep Recurrent Q Network
    Asynchronous Advantage Actor Critic Network
    Policy Gradients and Optimization
    Capstone Project – Car Racing using DQN
    Current Research and Next Steps