NOC:Reinforcement Learning (USB)

₹1,250.00
In stock
SKU
106106143



Media Storage Type : 64 GB USB Stick

NPTEL Subject Matter Expert : Dr. B. Ravindran

NPTEL Co-ordinating Institute : IIT Madras

NPTEL Lecture Count : 64

NPTEL Course Size : 36 GB

NPTEL PDF Text Transcription : Available and Included

NPTEL Subtitle Transcription : Available and Included (SRT)


Lecture Titles:

Lecture 1 - Tutorial 1 - Probability Basics 1
Lecture 2 - Tutorial 1 - Probability Basics 2
Lecture 3 - Tutorial 2 - Linear algebra - 1
Lecture 4 - Tutorial 2 - Linear algebra - 2
Lecture 5 - Introduction to RL
Lecture 6 - RL Framework and applications
Lecture 7 - Introduction to Immediate RL
Lecture 8 - Bandit Optimalities
Lecture 9 - Value function based methods
Lecture 10 - UCB 1
Lecture 11 - Concentration Bounds
Lecture 12 - UCB 1 Theorem
Lecture 13 - PAC Bounds
Lecture 14 - Median Elimination
Lecture 15 - Thompson Sampling
Lecture 16 - Policy Search
Lecture 17 - REINFORCE
Lecture 18 - Contextual Bandits
Lecture 19 - Full RL Introduction
Lecture 20 - Returns, Value Functions and MDPs
Lecture 21 - MDP Modelling
Lecture 22 - Bellman Equation
Lecture 23 - Bellman Optimality Equation
Lecture 24 - Cauchy Sequence and Green's Equation
Lecture 25 - Banach Fixed Point Theorem
Lecture 26 - Convergence Proof
Lecture 27 - Lpi Convergence
Lecture 28 - Value Iteration
Lecture 29 - Policy Iteration
Lecture 30 - Dynamic Programming
Lecture 31 - Monte Carlo
Lecture 32 - Control in Monte Carlo
Lecture 33 - Off Policy MC
Lecture 34 - UCT
Lecture 35 - TD(0)
Lecture 36 - TD(0) Control
Lecture 37 - Q-Learning
Lecture 38 - Afterstate
Lecture 39 - Eligibility Traces
Lecture 40 - Backward View of Eligibility Traces
Lecture 41 - Eligibility Trace Control
Lecture 42 - Thompson Sampling Recap
Lecture 43 - Function Approximation
Lecture 44 - Linear Parameterization
Lecture 45 - State Aggregation Methods
Lecture 46 - Function Approximation and Eligibility Traces
Lecture 47 - LSTD and LSTDQ
Lecture 48 - LSPI and Fitted Q
Lecture 49 - DQN and Fitted Q-Iteration
Lecture 50 - Policy Gradient Approach
Lecture 51 - Actor Critic and REINFORCE
Lecture 52 - REINFORCE (cont'd)
Lecture 53 - Policy Gradient with Function Approximation
Lecture 54 - Hierarchical Reinforcement Learning
Lecture 55 - Types of Optimality
Lecture 56 - Semi Markov Decision Processes
Lecture 57 - Options
Lecture 58 - Learning with Options
Lecture 59 - Hierarchical Abstract Machines
Lecture 60 - MAXQ
Lecture 61 - MAXQ Value Function Decomposition
Lecture 62 - Option Discovery
Lecture 63 - POMDP Introduction
Lecture 64 - Solving POMDP

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