Learn · Reinforcement Learning
Lecture 1 of 60Week 1
Introduction to RL
Prof. Balaraman Ravindran · IIT Madras
Credits and licence
- From
- Reinforcement Learning, IIT Madras (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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- Tweens profiles and older. How courses are placed
All the lectures
- Introduction to RL
- RL Framework and Applications
- Introduction to Immediate RL
- Bandit Optimalities
- Value Function Based Methods
- UCB 1
- Concentration Bounds
- UCB 1 Theorem
- PAC Bounds
- Median Elimination
- Thompson Sampling
- Policy Search
- REINFORCE
- Contextual Bandits
- Full RL Introduction
- Returns, Value Functions and MDPs
- MDP Modelling
- Bellman Equation
- Bellman Optimality Equation
- Cauchy Sequence and Green's Equation
- Banach Fixed Point Theorem
- Convergence Proof
- Lpi Convergence
- Value Iteration
- Policy Iteration
- Dynamic Programming
- Monte Carlo
- Control in Monte Carlo
- Off Policy MC
- UCT
- TD(0)
- TD(0) Control
- Q-Learning
- Afterstate
- Eligibility Traces
- Backward View of Eligibility Traces
- Eligibility Trace Control
- Thompson Sampling Recap
- Function Approximation
- Linear Parameterization
- State Aggregation Methods
- Function Approximation and Eligibility Traces
- LSTD and LSTDQ
- LSPI and Fitted Q
- DQN and Fitted Q-Iteration
- Policy Gradient Approach
- Actor Critic and REINFORCE
- REINFORCE (cont'd)
- Policy Gradient with Function Approximation
- Hierarchical Reinforcement Learning
- Types of Optimality
- Semi Markov Decision Processes
- Options
- Learning with Options
- Hierarchical Abstract Machines
- MAXQ
- MAXQ Value Function Decomposition
- Option Discovery
- POMDP Introduction
- Solving POMDP