Subject Detail

Sno	Back	regulation	branch	year	semester	subject_code	name	credits	category
1	Back	R23AIML	ARTIFICIAL INTELLIGENCE & MACHINE LEARNING (AI&ML)	3	1	23A39502	INTRODUCTION TO REINFORCEMENT LEARNING	3.0	Theory

Syllabus List

Sno	subject	description	note	syllabus_file
1	INTRODUCTION TO REINFORCEMENT LEARNING - 23A39502 (Theory)	23A39502	23A39502	Link

Book List

Sno	subject	title	author	publisher	edition	book_type	IMG	book_file	note
1	INTRODUCTION TO REINFORCEMENT LEARNING - 23A39502 (Theory)	Reinforcement Learning: An Introduction	Richard S. Sutton and Andrew G. Barto	MIT Press	2nd Edition 2018	Textbook		Link	note
2	INTRODUCTION TO REINFORCEMENT LEARNING - 23A39502 (Theory)	An Introduction to Deep Reinforcement Learning	François-Lavet et al.,	Foundations and Trends® in Machine Learning, 2018	None	Reference		Link	note
3	INTRODUCTION TO REINFORCEMENT LEARNING - 23A39502 (Theory)	Foundations of Deep Reinforcement Learning	Laura Graesser Wah Loon Keng	Pearson	None	Reference		Link	note
4	INTRODUCTION TO REINFORCEMENT LEARNING - 23A39502 (Theory)	Reinforcement Learning and Games	Aske Plaat	NOne	None	Reference		Link	note
5	INTRODUCTION TO REINFORCEMENT LEARNING - 23A39502 (Theory)	A Course in Reinforcement Learning 2nd Edition	None	None	2nd Edition	Reference		Link	note

OnlineReference List

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Question Paper List

Sno	Subject	Question Paper Type	Date	Title	File

Lab List

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