Markov Decision Processes and the Basics of Temporal Difference Learning

Courselet on Reinforcement Learning, giving an entrance level description on problem formulation as Markov Decision Processes, the three curses of dimensionality and basic temporal difference methods.

0 Rating
0 Reviews
0 Students Enrolled

Suggested citation

Heeswijk, Wouter van (2025). Markov Decision Processes and the Basics of Temporal Difference Learning. Q2 ecosystem. Available at https://quantinar.com/course/864/markov-decision-processes-and-the-basics-of-temporal-difference-learnin or http://dx.doi.org/10.24818/IDA-CL/2025.29.

@misc{heeswijk2025, author = {Heeswijk, Wouter van}, title = {Markov Decision Processes and the Basics of Temporal Difference Learning}, year = {2025}, howpublished = {Q2 ecosystem}, doi = {http://dx.doi.org/10.24818/IDA-CL/2025.29.}, url = {https://quantinar.com/course/864/markov-decision-processes-and-the-basics-of-temporal-difference-learnin} }
Wishlist

Add To Cart

Tags:

Courselet Content

2 components

	Markov Decision Processes and the Basics of Temporal Difference Learning (pdf)	2.82 M
	Markov Decision Processes and the Basics of Temporal Difference Learning (video)	76 min

Requirements

Patricipants in this course are expected to have a basic understanding of stochastic models and (partially observable) Markov Decision Processes. Furthermore, a certain degree of familiarity with programming (the course will use Python), dynamic programming, calculus and statistics is expected.

General Overview

Description

- Building blocks of Markov Decision Processes
- State-, action- and outcome spaces
- Post-decision states and look-up tables
- Introduction to temporal difference learning: Monte carlo learning, Q-learning and SARSA