B.E. (Artificial Intelligence and Data Science) Reinforcement Learning Syllabus - Mumbai University
This is the Fourth Year BE AI and DS syllabus under CBCS REV-2019 'C' Scheme, in force from the academic year 2023-24. The University has published no NEP 2020 syllabus for Semesters V to VIII of any engineering branch, so this is the scheme you are examined on — exam form 1T01817 and 1T01818. The first and second years of the degree are on NEP 2020.
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Syllabus for Reinforcement Learning
The University heads this subject CSDO8011, which is already the code of AI for Financial and Banking Application earlier in the same circular. Her own Department Optional Course-5 list prints Reinforcement Learning as CSDO8013.
Module Prerequisite 02 hours
- Prerequisite Probability distributions and expected values, and basic linear algebra (e.g., inner products).
- Introduction to Reinforcement Learning: Reinforcement Learning: Key features and Elements of RL, Types of RL, rewards. Reinforcement Learning Algorithms: Q-Learning, State Action Reward State action (SARSA),
- Bandit problems and online learning: An n-Armed Bandit Problem, Action-Value Methods Tracking a Nonstationary Problem, Optimistic Initial Values Upper-Confidence-Bound Action Selection
- Gradient Bandits
- Markov Decision Processes: The Agent–Environment Interface, The Agent–Environment Interface, Goals and Rewards, Returns, Markov properties, Markov Decision Process, Value Functions and Optimal Value Functions,
- Dynamic Programming: Policy Evaluation (Prediction), Policy Improvement, Policy Iteration, Value Iteration, Asynchronous Dynamic Programming, Generalized Policy Iteration
- Monte Carlo Methods and Temporal-Difference Learning Monte Carlo Prediction, Monte Carlo Estimation of Action Values, Monte Carlo Control, TD Prediction, TD control using Q-Learning
- Applications and Case Studies Elevator Dispatching, Dynamic Channel Allocation, Job-Shop Scheduling
Text Books
- 1 Reinforcement Learning: An Introduction, by Richard S. Sutton and Andrew G. Barto
- 2 Alessandro Palmas, Dr. Alexandra Galina Petre, Emanuele Ghelfi, The Reinforcement Learning Workshop: Learn how to Apply Cutting-edge Reinforcement Learning Algorithms to a Wide Range of Control Problems, 2020 Packt publishing.
- 3 Phil Winder, Reinforcement Learning Industrial Applications with Intelligent Agents, O'Reilly
- 4 Dr Engr S M Farrukh Akhtar, Practical Reinforcement Learning, Packt Publishing, 2017.
References
- 1 Maxim Lapan, Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero.
- 2 Alberto Leon-Garcia, Probability, Statistics and Random Processes for Electrical Engineering, Third Edition, Pearson Education, Inc
- 3 Csaba Szepesv ́ari, Algorithms for Reinforcement Learning, Morgan & Claypool Publishers
Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Data Science), item 6.12 (N), under CBCS REV-2019 'C' Scheme, in force from the academic year 2023-24. Wording, module numbering and hours are as printed in that syllabus.
The complete syllabus
This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.