Deep Reinforcement Learning with Python (Record no. 199938)

MARC details
000 -LEADER
fixed length control field 02033nam a22002417a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260428124857.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781839210686
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31 RAV-D
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Ravichandiran, Sudharsan
245 ## - TITLE STATEMENT
Title Deep Reinforcement Learning with Python
Remainder of title Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow
250 ## - EDITION STATEMENT
Edition statement 2nd.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication Mumbai
Name of publisher Packt Publishing
Year of publication 2020
300 ## - PHYSICAL DESCRIPTION
Number of Pages xxi, 730p.
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. 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.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Artificial intelligence
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Machine learning
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Topical Term Reinforcement learning (RL)
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books and Monographs
Holdings
Full call number Accession Number Koha item type Lost status Damaged status Permanent Location Current Location Shelving location Date acquired Source of acquisition Cost, normal purchase price
006.31 RAV-D 102740 Books and Monographs     Central Library, NIT Jalandhar Central Library, NIT Jalandhar General Stacks 27.04.2026 Mumbai, TV Enterprises 3699.00
006.31 RAV-D 102741 Books and Monographs     Central Library, NIT Jalandhar Central Library, NIT Jalandhar General Stacks 27.04.2026 Mumbai, TV Enterprises 3699.00
006.31 RAV-D 102742 Books and Monographs     Central Library, NIT Jalandhar Central Library, NIT Jalandhar General Stacks 27.04.2026 Mumbai, TV Enterprises 3699.00
006.31 RAV-D 102743 Books and Monographs     Central Library, NIT Jalandhar Central Library, NIT Jalandhar General Stacks 27.04.2026 Mumbai, TV Enterprises 3699.00
006.31 RAV-D 102744 Books and Monographs     Central Library, NIT Jalandhar Central Library, NIT Jalandhar General Stacks 27.04.2026 Mumbai, TV Enterprises 3699.00
006.31 RAV-D 102745 Books and Monographs     Central Library, NIT Jalandhar Central Library, NIT Jalandhar General Stacks 27.04.2026 Mumbai, TV Enterprises 3699.00
Dr. Sanjeev, Librarian
Managed by: Dr. D. P. Tripathi, Deputy Librarian, Central Library
For any query / question, please mail at circulation.liby@nitj.ac.in 

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