Deep Reinforcement Learning with Python : Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow (Record no. 51168)

MARC details
000 -LEADER
fixed length control field 01881 a2200193 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250823235030.0
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781839210686
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31 RAV
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Ravichandiran, Sudharsan
245 ## - TITLE STATEMENT
Title Deep Reinforcement Learning with Python : Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow
250 ## - EDITION STATEMENT
Edition statement 2
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc Packt Publishing
Date of publication, distribution, etc 2020
Place of publication, distribution, etc Mumbai
300 ## - PHYSICAL DESCRIPTION
Extent 730
520 ## - SUMMARY, ETC.
Summary, etc 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.<br/><br/>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.<br/><br/>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.<br/><br/>By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer science
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial Intelligence
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine Theory
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Book
Source of classification or shelving scheme Dewey Decimal Classification

No items available.