Reinforcement Learning Complete Revision for Exams | All Units Covered

Struggling to prepare for your Reinforcement Learning (RL) exam? This video is your one-stop solution! We cover all units of the RL syllabus in a structured, exam-focused manner, including important concepts, algorithms, and previous year questions (PYQs). Perfect for last-minute revision and scoring high marks!
What’s Inside?
Unit I: Basics of Probability, Linear Algebra, and Stochastic Multi-Armed Bandits
Quick revision of probability (random variables, expectation, distributions).
Linear algebra essentials (vectors, matrices, eigenvalues).
Multi-armed bandits: UCB, KL-UCB, Thompson Sampling (with examples).
Unit II: Markov Decision Processes (MDPs)
MDP components: States, actions, transitions, rewards.
Reward models: Infinite discounted, total, finite horizon, average.
Bellman’s optimality, value iteration, and policy iteration (solved examples).
Unit III: The Reinforcement Learning Problem
Prediction vs. control problems.
Model-based algorithms and Monte Carlo methods (step-by-step explanations).
Unit IV: Bootstrapping and Model-Free Control
TD(0) algorithm and convergence of Monte Carlo and TD(0).
Model-free control: Q-learning, Sarsa, Expected Sarsa (with PYQs).
Unit V: Advanced Topics in RL
n-step returns and TD(λ) algorithm.
Function approximation: Linear TD(λ), tile coding.
Policy search, policy gradients, and experience replay.
Case studies and real-world applications (exam-oriented examples)