Description: Further DetailsTitle: Deep Reinforcement Learning with PythonCondition: NewSubtitle: Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow, 2nd EditionEAN: 9781839210686ISBN: 9781839210686Publisher: Packt Publishing LimitedFormat: PaperbackRelease Date: 09/30/2020Description: An example-rich guide for beginners to start their reinforcement and deep reinforcement learning journey with state-of-the-art distinct algorithmsKey FeaturesCovers a vast spectrum of basic-to-advanced RL algorithms with mathematical explanations of each algorithmLearn how to implement algorithms with code by following examples with line-by-line explanationsExplore the latest RL methodologies such as DDPG, PPO, and the use of expert demonstrationsBook Description With significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit. In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples. The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research. By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects.What you will learnUnderstand core RL concepts including the methodologies, math, and codeTrain an agent to solve Blackjack, FrozenLake, and many other problems using OpenAI GymTrain an agent to play Ms Pac-Man using a Deep Q NetworkLearn policy-based, value-based, and actor-critic methodsMaster the math behind DDPG, TD3, TRPO, PPO, and many othersExplore new avenues such as the distributional RL, meta RL, and inverse RLUse Stable Baselines to train an agent to walk and play Atari gamesWho this book is for If you’re a machine learning developer with little or no experience with neural networks interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Basic familiarity with linear algebra, calculus, and the Python programming language is required. Some experience with TensorFlow would be a plus.Language: EnglishCountry/Region of Manufacture: GBItem Height: 93mmItem Length: 75mmAuthor: Sudharsan RavichandiranGenre: Computing & InternetISBN-10: 1839210680Release Year: 2020 Missing Information?Please contact us if any details are missing and where possible we will add the information to our listing.
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Book Title: Deep Reinforcement Learning with Python
Title: Deep Reinforcement Learning with Python
Subtitle: Master classic RL, deep RL, distributional RL, inverse RL, and mo
EAN: 9781839210686
ISBN: 9781839210686
Release Date: 09/30/2020
Release Year: 2020
Country/Region of Manufacture: GB
Item Height: 93mm
Genre: Computing & Internet
ISBN-10: 1839210680
Number of Pages: 760 Pages
Language: English
Publication Name: Deep Reinforcement Learning with Python : Master Classic RL, Deep RL, Distributional RL, Inverse RL, and More with OpenAI Gym and TensorFlow, 2nd Edition
Publisher: Packt Publishing, The Limited
Subject: Machine Theory, Intelligence (Ai) & Semantics, Neural Networks
Publication Year: 2020
Type: Textbook
Author: Sudharsan Ravichandiran
Subject Area: Computers
Item Length: 3.6 in
Item Width: 3 in
Format: Trade Paperback
