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Reinforcement Learning and Decision Making tutorials explained at an intuitive level and with Jupyter Notebooks
| Date | Stars |
|---|---|
| 2026-07-31 | 331 |
| 2026-08-01 | 331 |
| 2026-08-02 | 331 |
| 2026-08-06 | 331 |
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# Applied Reinforcement Learning
I've been studying reinforcement learning and decision-making for a couple of years now.
One of the most difficult things that I've encountered is not necessarily related to
the concepts but how these concepts have been explained. To me, learning occurs when one
is able to make a connection with the concepts being taught. For this, often an intuitive
explanation is required, and likely a hands-on approach helps build that kind of
understanding.
My goal for this repository is to create, with the community, a resource that would help
newcomers understand reinforcement learning in an intuitive way. Consider what you see here
my initial attempt to teach some of these concepts as plain and simple as I can possibly
explain them.
If you'd like to collaborate, whether a typo, or an entire addition to the text, maybe a fix
to a notebook or a whole new notebook, please feel free to send your issue and/or pull
request to make things better. As long as your pull request aligns with the goal of the
repository, it is very likely we will merge. I'm not the best teacher, or reinforcement
learning researcher, but I do believe we can make reinforcement learning and decision-making
easy for anyone to understand. Well, at least easier.
Table of Contents
=================
* [Notebooks Installation](#notebooks-installation)
* [Install git](#install-git)
* [Install Docker](#install-docker)
* [Run Notebooks](#run-notebooks)
* [TL;DR version](#tldr-version)
* [A little more detailed version:](#a-little-more-detailed-version)
* [Open the Notebooks in your browser:](#open-the-notebooks-in-your-browser)
* [Open TensorBoard at the following address:](#open-tensorboard-at-the-following-address)
* [Docker Tips](#docker-tips)
* [Part I: Introduction](01-introduction-to-decision-making/README.md#part-i-introduction)
* [1. Introduction to Decision-Making](01-introduction-to-decision-making/README.md#1-introduction-to-decision-making)
* [1.1 Decision-Making](01-introduction-to-decision-making/README.md#11-decision-making)
* [1.2 Further Reading](01-introduction-to-decision-making/README.md#12-further-reading)
* [Part II: Reinforcement Learning and Decision-Making](02-sequential-decisions/README.md#part-ii-reinforcement-learning-and-decision-making)
* [2. Sequential Decisions](02-sequential-decisions/README.md#2-sequential-decisions)
* [2.1 Modeling Decision-Making Problems](02-sequential-decisions/README.md#21-modeling-decision-making-problems)
* [2.2 Solutions Representation](02-sequential-decisions/README.md#22-solutions-representation)
* [2.3 Simple Sequential Problem](02-sequential-decisions/README.md#23-simple-sequential-problem)
* [2.4 Slightly more complex problems](02-sequential-decisions/README.md#24-slightly-more-complex-problems)
* [2.5 Evaluating solutions](02-sequential-decisions/README.md#25-evaluating-solutions)
* [2.6 Improving on solutions](02-sequential-decisions/README.md#26-improving-on-solutions)
* [2.7 Finding Optimal solutions](02-sequential-decisions/README.md#27-finding-optimal-solutions)
* [2.8 Improving on Policy Iteration](02-sequential-decisions/README.md#28-improving-on-policy-iteration)
* [2.9 Exercises](02-sequential-decisions/README.md#29-exercises)
* [2.10 Further Reading](02-sequential-decisions/README.md#210-further-reading)
* [3. Deterministic and Stochastic Actions](03-deterministic-and-stochastic-actions/README.md#3-deterministic-and-stochastic-actions)
* [3.1 We can't perfectly control the world](03-deterministic-and-stochastic-actions/README.md#31-we-cant-perfectly-control-the-world)
* [3.2 Dealing with stochasticity](03-deterministic-and-stochastic-actions/README.md#32-dealing-with-stochasticity)
* [3.3 Exercises](03-deterministic-and-stochastic-actions/README.md#33-exercises)
Excerpt of 11,808 characters
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