An Introduction to Deep Reinforcement Learning, Vinod K.M., 2026

An Introduction to Deep Reinforcement Learning, Vinod K.M., 2026.
        
   The current era of artificial intelligence and machine learning (AIML) tools has transformed the workings of vast swaths of our private, working, and social lives beyond recognition. It has been found that these tools can solve many problems in better and faster ways compared to humans. AIML tools allow machines and related systems to reason and infer almost like humans, and this has deep intellectual and philosophical ramifications as well. The areas of machine learning are broadly classified into supervised, unsupervised, and deep reinforcement learning (DRL). The last one comes closest to how humans reason, and various innovations in this area have many useful applications.
This book covers most of the areas of DRL, with a special focus on its mathematical and algorithmic foundations. Undergraduate and early graduate students should find it to be a good guide to the fast-developing areas of DRL and its myriad applications in both technical and social contexts.




Single-Agent Algorithms.
The historical development of RL started with the situations where the number of possible states and actions were finite and discrete. Usually, these could be presented in a tabular form. Many games of strategy like Go and Chess fall under this category. Neural networks are not needed for their solution, so they are not deep RL but simply RL algorithms.

There are many approaches to RL depending on which aspect is emphasized. This leads to many algorithms, some of which are very general, and others better suited to specific problems. We start with general considerations applicable to the classification of RL algorithms by understanding the applicable environment.

Contents.
Prologue.
Chapter 1 Introduction.
Chapter 2 Survey of ML.
Chapter 3 Basic Mathematics behind Deep Reinforcement Learning.
Chapter 4 Single-Agent Algorithms.
Chapter 5 Multi- Agent RL (MARL) Algorithms.
Chapter 6 Recent Developments in DRL.
Chapter 7 Applications of RL.
EPILOGUE.
ACKNOWLEDGMENTS.
BIBLIOGRAPHY.
INDEX.



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