B.E Artificial Intelligence and Machine Learning Reinforcement Learning Lab Syllabus - Mumbai University
This is the Fourth Year AI-ML syllabus under REV-2019 'C' Scheme, in force from the academic year 2023-24. The University has not yet published an NEP 2020 syllabus for this year of the degree, and this is the scheme its examinations are set on.
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Syllabus for Reinforcement Learning Lab
Suggested List of Experiments
- 1 Implementing a simple grid-world environment and training an agent using basic Q learning
- 2 Implementing a multi-armed bandit problem and comparing different exploration strategies like epsilon-greedy and UCB. 3, Implementing a basic grid-world environment as an MDP and applying policy iteration and value iteration algorithms to find optimal policies.
- 4 Applying dynamic programming algorithms, such as policy evaluation and policy improvement, to solve a small-scale MDP problem.
- 5 Implementing Monte Carlo control and Temporal Difference (TD) learning algorithms to train an agent in a grid-world environment.
- 6 Exploration vs. Exploitation Trade-off: Experimenting with different exploration strategies and analyzing their impact on the learning performance of an agent in a bandit problem.
- 7 Function Approximation in Reinforcement Learning: Using function approximation techniques, such as linear regression or neural networks, to approximate value functions in reinforcement learning problems.
- 8 Deep Reinforcement Learning: Implementing a deep Q-network (DQN) to train an agent to play a popular Atari game, such as Pong or Space Invaders.
- 9 Transfer Learning and Multi-Task Reinforcement Learning: Investigating transfer learning in reinforcement learning by training an agent in one environment and transferring its knowledge to a different but related environment
- 10 Policy Gradient Methods: Implementing policy gradient methods, such as REINFORCE or Proximal Policy Optimization (PPO), to train an agent in a continuous control environment. *11. Applications and Case Studies: Applying reinforcement learning techniques to solve a real-world problem, such as training a self-driving car to navigate a simulated road environment.
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 Books:
- 1 Maxim Lapan, Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero.
- 2 Csaba Szepesv´ari, Algorithms for Reinforcement Learning, Morgan & Claypool Publishers
- 3 Alberto Leon-Garcia, Probability, Statistics and Random Processes for Electrical Engineering, Third Edition, Pearson Education, Inc.
- 1 Machine Learning and Friends at Carnegie Mellon University
- 2 Reinforcement Learning: A Survey
- 3 Bibliography on Reinforcement Learning
- 4 David J. Finton's Reinforcement Learning Page
Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Machine Learning) under REV-2019 'C' Scheme, in force from the academic year 2023-24. Wording is as printed in that syllabus. The University sets no module list for this course; what she publishes is the objectives, outcomes and assessment guidelines reproduced above.
The complete syllabus
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