Sanghi Deep Reinforcement Learning with Python

With PyTorch, TensorFlow and OpenAI Gym
1. Auflage 2021
ISBN: 978-1-4842-6809-4
Verlag: APRESS
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 382 Seiten

Reihe: Professional and Applied Computing (R0)

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 inthe 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.
What You'll Learn

• Examine deep reinforcement learning 
• Implement deep learning algorithms using OpenAI’s Gym environment
• Code your own game playing agents for Atari using actor-critic algorithms
• Apply best practices for model building and algorithm training 

Who This Book Is For
Machine learning developers and architects who want to stay ahead of the curve in the field of AI and deep learning.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Sanghi, Nimish

Inhaltsverzeichnis

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.

Über Autor(innen)

Nimish is a passionate technical leader who brings to table extreme focus on use of technology for solving customer problems. He has over 25 years of work experience in the Software and Consulting. Nimish has held leadership roles with P&L responsibilities at PwC, IBM and Oracle. In 2006 he set out on his entrepreneurial journey in Software consulting at SOAIS with offices in Boston, Chicago and Bangalore. Today the firm provides Automation and Digital Transformation services to Fortune 100 companies helping them make the transition from on-premise applications to the cloud.

 

He is also an angel investor in the space of AI and Automation driven startups. He has co-founded Paybooks, a SaaS HR and Payroll platform for Indian market. He has also cofounded a Boston based startup which offers ZipperAgent and ZipperHQ, a suite of AI driven workflow and video marketing automation platforms. He currently hold the position as CTO and Chief Data Scientist for both these platforms. 

Nimish has an MBA from Indian Institute of Management in Ahmedabad, India and a BS in Electrical Engineering from Indian Institute of Technology in Kanpur, India. He also holds multiple certifications in AI and Deep Learning.