Reinforcement Learning
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Reinforcement Learning Syllabus — Unit-wise Content
The official GTU curriculum for Reinforcement Learning (subject code 3174208) as prescribed for CS-DS · Semester 7 divides the course into 6 units, covering 38 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (3174208)Unit 1 Introduction
- Course logistics and overview
- Origin and history of Reinforcement Learning research
- Its connections with other related fields and with different branches of machine learning
Unit 2 Probability Primer
- Brush up of Probability concepts - Axioms of probability
- concepts of random variables, PMF, PDFs
- Concepts of joint and multiple random variables
- conditional and marginal distributions
- Correlation and independence
Unit 3 Markov Decision Process
- Introduction to RL terminology
- Markov property
- Markov chains
- Markov reward process (MRP)
- Introduction to and proof of Bellman equations for MRPs along with proof of existence of solution to Bellman equations in MRP
- Introduction to Markov decision process (MDP)
- state Subject Code : 3174208 Subject Name : Reinforcement Learning and action value functions
- Bellman expectation equations
- optimality of value functions and policies
- Bellman optimality equations
Unit 4 Prediction and Control by Dynamic Programing
- Overiew of dynamic programing for MDP, definition and formulation of planning in MDPs
- principle of optimality
- iterative policy evaluation
- policy iteration
- value iteration
- Banach fixed point theorem
- proof of contraction mapping property of Bellman expectation and optimality operators
- proof of convergence of policy evalutation and value iteration algorithms
- DP extensions
Unit 5 Monte Carlo Methods for Model Free Prediction and Control
- Overiew of Monte Carlo methods for model free RL, First visit and every visit Monte Carlo
- Monte Carlo control
- On policy and off policy learning
- Importance sampling
Unit 6 TD Methods
- Incremental Monte Carlo Methods for Model Free Prediction
- Overview TD(0)
- TD(1) and TD(λ)
- k-step estimators
- unified view of DP, MC and TD evaluation methods
- TD Control methods - SARSA
- Q-Learning and their variants
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