Cargando…
Deep reinforcement learning with Python : with Pytorch, Tensorflow and OpenAI Gym /
Deep reinforcement learning is a fast-growing discipline that is making a significant impact in fields of autonomous vehicles, robotics, healthcare, finance, and many more. This book covers deep reinforcement learning using deep-q learning and policy gradient models with coding exercise. You'll...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
Apress,
2021.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
| LEADER | 00000cam a2200000 a 4500 | ||
|---|---|---|---|
| 001 | OR_on1244805565 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 210405s2021 xxu o 001 0 eng d | ||
| 040 | |a YDX |b eng |e pn |c YDX |d GW5XE |d EBLCP |d OCLCO |d OCLCF |d UKAHL |d OCLCO |d OCLCQ |d COM |d N$T |d OCLCQ |d OCLCO | ||
| 019 | |a 1245668054 | ||
| 020 | |a 9781484268094 |q (electronic bk.) | ||
| 020 | |a 1484268091 |q (electronic bk.) | ||
| 020 | |z 1484268083 | ||
| 020 | |z 9781484268087 | ||
| 024 | 7 | |a 10.1007/978-1-4842-6809-4 |2 doi | |
| 029 | 1 | |a AU@ |b 000069095396 | |
| 035 | |a (OCoLC)1244805565 |z (OCoLC)1245668054 | ||
| 050 | 4 | |a Q325.6 | |
| 072 | 7 | |a COM004000 |2 bisacsh | |
| 082 | 0 | 4 | |a 006.3/1 |2 23 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Sanghi, Nimish. | |
| 245 | 1 | 0 | |a Deep reinforcement learning with Python : |b with Pytorch, Tensorflow and OpenAI Gym / |c Nimish Sanghi. |
| 260 | |a [Place of publication not identified] : |b Apress, |c 2021. | ||
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 505 | 0 | |a Chapter 1: Introduction to Deep Reinforcement Learning -- Chapter 2: Markov Decision Processes -- Chapter 3: Model Based Algorithms -- Chapter 4: Model Free Approaches -- Chapter 5: Function Approximation -- Chapter 6:Deep Q-Learning -- Chapter 7: Policy Gradient Algorithms -- Chapter 8: Combining Policy Gradients and Q-Learning -- Chapter 9: Integrated Learning and Planning -- Chapter 10: Further Exploration and Next Steps. | |
| 520 | |a Deep reinforcement learning is a fast-growing discipline that is making a significant impact in fields of autonomous vehicles, robotics, healthcare, finance, and many more. This book covers deep reinforcement learning using deep-q learning and policy gradient models with coding exercise. You'll begin by reviewing the Markov decision processes, Bellman equations, and dynamic programming that form the core concepts and foundation of deep reinforcement learning. Next, you'll study model-free learning followed by function approximation using neural networks and deep learning. This is followed by various deep reinforcement learning algorithms such as deep q-networks, various flavors of actor-critic methods, and other policy-based methods. You'll also look at exploration vs exploitation dilemma, a key consideration in reinforcement learning algorithms, along with Monte Carlo tree search (MCTS), which played a key role in the success of AlphaGo. The final chapters conclude with deep reinforcement learning implementation using popular deep learning frameworks such as TensorFlow and PyTorch. In the end, you'll understand deep reinforcement learning along with deep q networks and policy gradient models implementation with TensorFlow, PyTorch, and Open AI Gym. You will: Examine deep reinforcement learning Implement deep learning algorithms using OpenAIs Gym environment Code your own game playing agents for Atari using actor-critic algorithms Apply best practices for model building and algorithm training. | ||
| 500 | |a Includes index. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Reinforcement learning. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 6 | |a Apprentissage par renforcement (Intelligence artificielle) | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 650 | 7 | |a Reinforcement learning |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Sanghi, Nimish. |t Deep reinforcement learning with Python. |d [Place of publication not identified] : Apress, 2021 |z 1484268083 |z 9781484268087 |w (OCoLC)1223069075 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781484268094/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a Askews and Holts Library Services |b ASKH |n AH38627743 | ||
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL6533449 | ||
| 938 | |a YBP Library Services |b YANK |n 302032660 | ||
| 938 | |a EBSCOhost |b EBSC |n 2903515 | ||
| 994 | |a 92 |b IZTAP | ||