BOOKS - Deep Reinforcement Learning and Its Industrial Use Cases AI for Real-World Ap...
Deep Reinforcement Learning and Its Industrial Use Cases AI for Real-World Applications - Shubham Mahajan, Pethuru Raj, Amit Kant Pandit 2025 PDF Wiley-Scrivener BOOKS
ECO~18 kg CO²

1 TON

Views
748162

 
Deep Reinforcement Learning and Its Industrial Use Cases AI for Real-World Applications
Author: Shubham Mahajan, Pethuru Raj, Amit Kant Pandit
Year: 2025
Pages: 403
Format: PDF
File size: 46.6 MB
Language: ENG



Deep Reinforcement Learning and Its Industrial Use Cases AI for Real-World Applications Introduction The rapid development of artificial intelligence (AI) has had a significant impact on various industries, including manufacturing, healthcare, finance, and transportation. One of the most promising areas of AI research is deep reinforcement learning, which combines the power of deep learning with the flexibility of reinforcement learning to create more sophisticated and effective algorithms. This book provides an overview of the current state of deep reinforcement learning and its industrial use cases, highlighting the potential benefits and challenges of this technology. Chapter 1: Understanding Deep Reinforcement Learning In this chapter, we explore the fundamentals of deep reinforcement learning, including the history of the field, key concepts such as Q-learning and policy gradients, and the importance of exploration and exploitation trade-offs. We also discuss the limitations of traditional reinforcement learning methods and how deep reinforcement learning addresses these limitations.
''

You may also be interested in:

Deep Reinforcement Learning and Its Industrial Use Cases AI for Real-World Applications
Deep Reinforcement Learning and Its Industrial Use Cases AI for Real-World Applications
Artificial Intelligence What You Need to Know About Machine Learning, Robotics, Deep Learning, Recommender Systems, Internet of Things, Neural Networks, Reinforcement Learning, and Our Future
Python AI Programming: Navigating fundamentals of ML, deep learning, NLP, and reinforcement learning in practice
Python AI Programming Navigating fundamentals of ML, Deep Learning, NLP, and reinforcement learning in practice
Python AI Programming Navigating fundamentals of ML, Deep Learning, NLP, and reinforcement learning in practice
TensorFlow for Deep Learning From Linear Regression to Reinforcement Learning
Deep Reinforcement Learning
Deep Reinforcement Learning with Python, 2E
Deep Reinforcement Learning in Action
Deep Reinforcement Learning in Action
Practical Deep Reinforcement Learning with Python
Grokking Deep Reinforcement Learning (Final Edition)
Foundations of Deep Reinforcement Learning Theory and Practice in Python
Deep Reinforcement Learning with Python: With PyTorch, TensorFlow and OpenAI Gym
Deep Reinforcement Learning for Wireless Communications and Networking: Theory, Applications and Implementation
Deep Reinforcement Learning for Wireless Communications and Networking Theory, Applications and Implementation
Foundations of Deep Reinforcement Learning Theory and Practice in Python (Rough Cuts)
Deep Reinforcement Learning for Reconfigurable Intelligent Surfaces and UAV Empowered Smart 6G Communications
Deep Reinforcement Learning with Python RLHF for Chatbots and Large Language Models, 2nd Edition
Deep Reinforcement Learning with Python RLHF for Chatbots and Large Language Models, 2nd Edition
Hands-On Intelligent Agents with OpenAI Gym: Your guide to developing AI agents using deep reinforcement learning
Deep Learning Techniques for Automation and Industrial Applications
Deep Learning Techniques for Automation and Industrial Applications
Deep Learning for Data Architects: Unleash the power of Python|s deep learning algorithms (English Edition)
Java Deep Learning Projects: Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs
Deep Learning for the Life Sciences Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More First Edition
Building Scalable Deep Learning Pipelines on AWS Develop, Train, and Deploy Deep Learning Models
Getting started with Deep Learning for Natural Language Processing Learn how to build NLP applications with Deep Learning
Deep Learning fur die Biowissenschaften Einsatz von Deep Learning in Genomik, Biophysik, Mikroskopie und medizinischer Analyse
Deep Learning for Data Architects Unleash the power of Python|s deep learning algorithms
Anatomy of Deep Learning Principles: Writing a deep learning library from scratch (Japanese Edition)
Deep Learning for Finance Creating Machine & Deep Learning Models for Trading in Python
Deep Learning for the Life Sciences Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More
Deep Learning for Finance Creating Machine & Deep Learning Models for Trading in Python
Deep Learning for Data Architects Unleash the power of Python|s deep learning algorithms
Distributional Reinforcement Learning (Adaptive Computation and Machine Learning)
Statistical Reinforcement Learning Modern Machine Learning Approaches
Programming PyTorch for Deep Learning Creating and Deploying Deep Learning Applications First Edition
Deep Learning With Python Develop Deep Learning Models on Theano and TensorFlow using Keras