Farid
Reinforcement Learning
by Ajit Singh
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ISBN: B0FF36KRJQ • Publisher: Manning Publications Co. • Year: 2026
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📂 [color=#ff5555]Category: Artificial Intelligence Expert Systems, Information Technology, Computers & Technology
🌐 [color=#55acee]Language: English
📄 [color=#44bb44]Pages: 312
Description: This textbook, " Reinforcement Learning ," is conceived with the vision of providing a comprehensive, accessible, and contemporary introduction to this fascinating field. It is meticulously designed to cater to the learning needs of undergraduate (B.Tech) and postgraduate (M.Tech) students in Computer Science, Artificial Intelligence, Data Science, and related engineering disciplines. Recognizing the evolving landscape of technical education, this book is strictly aligned with the ethos of the National Education Policy (NEP) 2020 and the guidelines set forth by the All India Council for Technical Education (AICTE). Furthermore, its curriculum is benchmarked against leading international university syllabi to ensure global relevance and competitiveness for students.
Key Features:
1. Comprehensive Coverage: Balances breadth and depth across core and advanced RL topics within a concise 10-chapter structure.
2. Progressive Learning Path: Builds understanding кирпич за кирпичом (brick by brick), from basic principles to complex algorithms.
3. Conceptual Clarity: Emphasizes intuitive explanations of complex mathematical concepts, supported by diagrams and illustrative examples.
4. Practical Insights: Includes pseudo-code for key algorithms, discussions on implementation considerations, and connections to real-world scenarios. (In a full book, this would involve code snippets and case studies).
5. Focus on Deep RL: Dedicates significant attention to Deep Reinforcement Learning, reflecting its current prominence in the field.
6. Ethical Considerations: Integrates discussions on the ethical implications and societal impact of RL, aligning with responsible AI development principles.
7. Updated Content: Incorporates recent advancements and important algorithms like PPO, DDPG, and discusses emerging areas like MARL and Offline RL.
8. Foundation for Further Study: Provides a strong base for students wishing to pursue advanced research or specialized applications in RL.
"Reinforcement Learning" is more than just an academic text; it is an invitation to explore the art and science of intelligent decision-making. It is designed to equip students with the knowledge and skills to contribute meaningfully to the ongoing AI revolution, fostering a generation of innovators who can harness RL’s power responsibly and effectively.
Disclaimer : Earnest request from the Author.
Kindly go through the table of contents and refer kindle edition for a glance on the related contents.
Thank you for your kind consideration!
📦 [color=#ff9900]Download Info
Folder: Reinforcement Learning From Human Feedback Alignment And Post Training Of LLMs Final Version
Format: PDF
Total Size: 23.4 MB
📋 File List:
[size=2]
📌 Reinforcement_Learning_from_Human_Feedba.epub (Ajit Singh) (2026) (10.08 MB)
📌 Reinforcement_Learning_from_Human_Feedba.pdf (Ajit Singh) (2025) (10.24 MB)
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