Reinforcement Learning. Instructor: Prof. Balaraman Ravindran, Department of Computer Science and Engineering, IIT Madras.
0
September 26, 2023
English
English [CC]
Description
Reinforcement learning is a paradigm that aims to model the trial-and-error learning process that is needed in many problem situations where explicit instructive signals are not available. It has roots in operations research, behavioral psychology and AI. The goal of the course is to introduce the basic mathematical foundations of reinforcement learning, as well as highlight some of the recent directions of research. (from nptel.ac.in)
Course Curriculum
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- Lecture 01 – Probability Basics 1 Unlimited
- Lecture 02 – Probability Basics 2 Unlimited
- Lecture 03 – Linear Algebra 1 Unlimited
- Lecture 04 – Linear Algebra 2 Unlimited
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- Lecture 05 – Introduction to RL Unlimited
- Lecture 06 – RL Framework and Applications Unlimited
- Lecture 07 – Introduction to Immediate RL Unlimited
- Lecture 08 – Bandit Optimalities Unlimited
- Lecture 09 – Value Function based Methods Unlimited
- Lecture 10 – Upper Confidence Bound 1 (UCB 1) Unlimited
- Lecture 11 – Concentration Bounds Unlimited
- Lecture 12 – UCB 1 Theorem Unlimited
- Lecture 13 – Probably Approximately Correct (PAC) Bounds Unlimited
- Lecture 14 – Median Elimination Unlimited
- Lecture 15 – Thompson Sampling Unlimited
- Lecture 21 – MDP Modelling Unlimited
- Lecture 22 – Bellman Equation Unlimited
- Lecture 23 – Bellman Optimality Equation Unlimited
- Lecture 24 – Cauchy Sequence and Green’s Equation Unlimited
- Lecture 25 – Banach Fixed Point Theorem Unlimited
- Lecture 26 – Convergence Proof Unlimited
- Lecture 33 – Off Policy MC Unlimited
- Lecture 34 – UCT (Upper Confidence Bound 1 applied to Trees) Unlimited
- Lecture 35 – TD (0) Unlimited
- Lecture 36 – TD (0) Control Unlimited
- Lecture 37 – Q-Learning Unlimited
- Lecture 38 – Afterstate Unlimited
- Lecture 43 – Function Approximation Unlimited
- Lecture 44 – Linear Parameterization Unlimited
- Lecture 45 – State Aggregation Methods Unlimited
- Lecture 46 – Function Approximation and Eligibility Traces Unlimited
- Lecture 47 – Least-Squares Temporal Difference (LSTD) and LSTDQ Unlimited
- Lecture 48 – LSPI and Fitted Q Unlimited
- Lecture 54 – Hierarchical Reinforcement Learning Unlimited
- Lecture 55 – Types of Optimality Unlimited
- Lecture 56 – Semi Markov Decision Processes Unlimited
- Lecture 57 – Options Unlimited
- Lecture 58 – Learning with Options Unlimited
- Lecture 59 – Hierarchical Abstract Machines Unlimited
- Lecture 63 – POMDP Introduction Unlimited
- Lecture 64 – Solving POMDP Unlimited
About the instructor
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