Here are the slides from Lectures
8_Continuous Time Dynamic Programming
Please read Section 2.1 of the notes
LQR and Kalman Filter
Here are the slides from Lectures
Please read these notes [which will be later added to the main set of notes]:
ODE method for Stochastic Approximation
We consider the Robbins-Monro update
and argue that this can be approximated by the o.d.e.:
Lyapunov Functions
Lyapunov functions are an extremely convenient device for proving that a dynamical system converges. We cover:
- The Lyapunov argument
- La Salle’s Invariance Principle
- An Alternative argument for Convex Functions
- Exponential Convergence Rates
Optimal Stopping
Here are the slides from Lectures
Please read Section 1.6 from the notes:
Please attempt Ex53, Ex54, Ex56, Ex57.
Algorithms for MDPs
Here are the slides from Lectures
Please read Section 1.5 from the notes:
Please attempt Ex39, 40 & 41 [if you can code], 42 and 43.
Infinite Time Horizon MDP
Here are the slides from Lectures
Please read Section 1.4 from the notes:
Please attempt Ex35, Ex36, Ex37.
Markov Decision Processes
Here are the slides from Lectures
3_Markov Decision Processes [pdf]
Please read Section 1.3 from the notes:
Please attempt exercises Ex22, Ex23, Ex24, Ex25.
Markov Chains
Here are the slides from Lectures
Please read Section 1.2 from the notes:
Dynamic Programming
Slides from Lectures are here:
Please read Section 1.1 of the notes:
Stochastic Control Notes [pdf]
Please attempt exercises Ex3, Ex5 and Ex6 from the notes.
Applied Probability Notes 