Modules / Lectures
Module NameDownloadDescriptionDownload Size
Week 0 - Preparatory MaterialWeek 0 Assignment 1Week 0 Assignment 1275 kb
Week 1 - Introduction to RL and Immediate RLWeek 1 Assignment 1Week 1 Assignment 1190 kb
Week 2 - Bandit AlgorithmsWeek 2 Assignment 1Week 2 Assignment 1234 kb
Week 3 - Policy Gradient Methods & Introduction to Full RLWeek 3 Assignment 1Week 3 Assignment 1216 kb
Week 4 - MDP Formulation, Bellman Equations & Optimality ProofsWeek 4 Assignment 1Week 4 Assignment 1297 kb
Week 5 - Dynamic Programming & Monte Carlo MethodsWeek 5 Assignment 1Week 5 Assignment 1213 kb
Week 6 - Monte Carlo & Temporal Difference MethodsWeek 6 Assignment 1Week 6 Assignment 1216 kb
Week 7 - Eligibility TracesWeek 7 Assignment 1Week 7 Assignment 1205 kb
Week 8 - Function ApproximationWeek 8 Assignment 1Week 8 Assignment 1205 kb
Week 9 - DQN, Fitted Q & Policy Gradient ApproachesWeek 9 Assignment 1Week 9 Assignment 1202 kb
Week 10 - Hierarchical Reinforcement LearningWeek 10 Assignment 1Week 10 Assignment 1190 kb
Week 11 - Hierarchical RL: MAXQWeek 11 Assignment 1Week 11 Assignment 1191 kb
Week 12 - POMDPsWeek 12 Assignment 1Week 12 Assignment 1171 kb


Sl.No Chapter Name MP4 Download Transcript Download
1Tutorial 1 - Probability Basics 1DownloadDownload
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2Tutorial 1-Probability basics2DownloadDownload
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3Tutorial 2-Linear algebra-1DownloadDownload
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4Tutorial 2-Linear algebra-2DownloadDownload
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5Introduction to RLDownloadDownload
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6RL Framework and applicationsDownloadDownload
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7Introduction to Immediate RLDownloadDownload
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8Bandit OptimalitiesDownloadDownload
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9Value function based methodsDownloadDownload
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10UCB 1DownloadDownload
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11Concentration BoundsDownloadDownload
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12UCB 1 TheoremDownloadDownload
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13PAC BoundsDownloadDownload
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14Median EliminationDownloadDownload
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15Thompson SamplingDownloadDownload
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16Policy SearchDownloadDownload
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17REINFORCEDownloadDownload
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18Contextual BanditsDownloadDownload
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19Full RL IntroductionDownloadDownload
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20Returns, Value Functions and MDPsDownloadDownload
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21MDP ModellingDownloadDownload
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22Bellman EquationDownloadDownload
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23Bellman Optimality EquationDownloadDownload
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24Cauchy Sequence and Green's EquationDownloadDownload
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25Banach Fixed Point TheoremDownloadDownload
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26Convergence ProofDownloadDownload
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27Lpi ConvergenceDownloadDownload
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28Value IterationDownloadDownload
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29Policy IterationDownloadDownload
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30Dynamic ProgrammingDownloadDownload
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31Monte CarloDownloadDownload
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32Control in Monte CarloDownloadDownload
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33Off Policy MCDownloadDownload
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34UCTDownloadDownload
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35TD(0)DownloadDownload
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36TD(0) ControlDownloadDownload
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37Q-LearningDownloadDownload
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38AfterstateDownloadDownload
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39Eligibility TracesDownloadDownload
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40Backward View of Eligibility TracesDownloadDownload
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41Eligibility Trace ControlDownloadDownload
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42Thompson Sampling RecapDownloadDownload
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43Function ApproximationDownloadDownload
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44Linear ParameterizationDownloadDownload
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45State Aggregation MethodsDownloadDownload
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46Function Approximation and Eligibility TracesDownloadDownload
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47LSTD and LSTDQDownloadDownload
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48LSPI and Fitted QDownloadDownload
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49DQN and Fitted Q-IterationDownloadDownload
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50Policy Gradient ApproachDownloadDownload
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51Actor Critic and REINFORCEDownloadDownload
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52REINFORCE (cont'd)DownloadDownload
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53Policy Gradient with Function ApproximationDownloadDownload
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54Hierarchical Reinforcement LearningDownloadDownload
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55Types of OptimalityDownloadDownload
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56Semi Markov Decision ProcessesDownloadDownload
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57OptionsDownloadDownload
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58Learning with OptionsDownloadDownload
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59Hierarchical Abstract MachinesDownloadDownload
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60MAXQDownloadDownload
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61MAXQ Value Function DecompositionDownloadDownload
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62Option DiscoveryDownloadDownload
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63POMDP IntroductionDownloadDownload
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64Solving POMDPDownloadDownload
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65Live SessionDownloadDownload
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