Workshops investigating the fundamental topic of Markov decision processes.

Markov Decision Processes

Markov decision processes (MDP) are fundamental to reinforcement learning. It defines a framework that allows us to design problems that can be solved by reinforcement learning. This page contains workshops investigating MDPs.

Presentation: A Code-Driven introduction to Reinforcement Learning

Phil Winder, Nov 2020

The slides and video below are the presentation that accompanies the code-driven introduction to RL notebook. Abstract Reinforcement learning (RL) is lined up to become the hottest new artificial intelligence paradigm in the next few years. Building upon machine learning, reinforcement learning has the potential to automate strategic-level thinking in industry. In this presentation I present a code-driven introduction to RL, where you will explore a fundamental framework called the Markov decision process (MDP) and learn how to build an RL algorithm to solve it.

Code-Driven Introduction to Reinforcement Learning

Phil Winder, Nov 2020

Welcome, this is an example from the book Reinforcement Learning, by Dr. Phil Winder. In this notebook you will be investigating the fundamentals of reinforcement learning (RL). The first section describes the Markov decision process (MDP), which is a framework to help you design problems. The second section formulates an RL-driven solution for the MDP. Prerequisites This notebook was developed to work in Binder or Google’s colabratory – other notebook hosts are available.