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SL_2024.html
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<title>Sequential Learning</title>
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<h1 id="logo"><a href="#">Sequential Learning (2024-2025)</a></h1>
<p>Rémy Degenne</p>
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<h3>Sequential learning</h3>
A course about reinforcement learning and bandits.
<ul>
<li><b>Lecture 1</b> - <a href="https://remydegenne.github.io/docs/SL_2024/Cours1.pdf">Reinforcement learning</a></li>
<li><b>Lecture 2</b> - <a href="https://remydegenne.github.io/docs/SL_2024/Cours2.pdf">Dynamic Programming</a></li>
<li><b>Lecture 3</b> - <a href="https://remydegenne.github.io/docs/SL_2024/Cours3.pdf">Reinforcement Learning Algorithms</a></li>
<li><b>Practical Session 1</b> - <a href="https://remydegenne.github.io/docs/SL_2024/readme.txt">installation readme</a> and <a href="https://remydegenne.github.io/docs/SL_2024/StoreManagement.ipynb">notebook</a>. (You can also use Google colab: <a target="_blank" href="https://colab.research.google.com/github/RemyDegenne/remydegenne.github.io/blob/master/docs/SL_2024/StoreManagement.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>) Deadline: Friday, December 6, 11:59 AM CET.</li>
<li><b>Lecture 4</b> - <a href="https://remydegenne.github.io/docs/SL_2024/Cours4.pdf">Reinforcement Learning with Function Approximation</a></li>
<li><b>Lecture 4.5</b> - <a href="https://remydegenne.github.io/docs/SL_2024/Cours4demi.pdf">Summary of the first 4 courses</a></li>
<li><b>Lecture 5</b> - <a href="https://remydegenne.github.io/docs/SL_2024/Cours5.pdf">Beyond Value-Based Methods</a></li>
<li><b>Practical Session 2</b> - <a href="https://remydegenne.github.io/docs/SL_2024/readme.txt">installation readme</a> and <a href="https://remydegenne.github.io/docs/SL_2024/TD0_QLearning.ipynb">notebook</a>. (You can also use Google colab: <a target="_blank" href="https://colab.research.google.com/github/RemyDegenne/remydegenne.github.io/blob/master/docs/SL_2024/TD0_QLearning.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>) Deadline: Friday, January 10, 15:00 CET.</li>
<li><b>Lecture 6</b> - <a href="https://remydegenne.github.io/docs/SL_2024/Cours6.pdf">Multi-armed bandits</a></li>
<li><b>Practical Session 3</b> - <a href="https://remydegenne.github.io/docs/SL_2024/readme.txt">installation readme</a> and <a href="https://remydegenne.github.io/docs/SL_2024/Bandit.ipynb">notebook</a>. (You can also use Google colab: <a target="_blank" href="https://colab.research.google.com/github/RemyDegenne/remydegenne.github.io/blob/master/docs/SL_2024/Bandit.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>) Deadline: Friday, January 31, 15:00 CET.</li>
<li><b>Lecture 7</b> - <a href="https://remydegenne.github.io/docs/SL_2024/Cours8.pdf">Best arm identification in bandits</a></li>
<li><b>Practical Session 4</b> - <a href="https://remydegenne.github.io/docs/SL_2024/readme_FittedQ.txt">installation readme (not the same as the previous ones!)</a> and <a href="https://remydegenne.github.io/docs/SL_2024/FittedQ.ipynb">notebook</a> (You can also use Google colab: <a target="_blank" href="https://colab.research.google.com/github/RemyDegenne/remydegenne.github.io/blob/master/docs/SL_2024/FittedQ.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>). Deadline: Friday, February 7, 15:00 CET.</li>
<li><b>Lecture 8</b> - <a href="https://remydegenne.github.io/docs/SL_2024/Cours9.pdf">Bandit tools for reinforcement Learning</a></li>
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<h4>References</h4>
<ul>
<li><a href="https://tor-lattimore.com/downloads/book/book.pdf">Bandit Algorithms</a>. Tor Lattimore and Csaba Szepesvari (2019).</li>
<li><a href="http://www.incompleteideas.net/book/the-book.html">Reinforcement Learning</a>. Richard Sutton and Andrew Barto (2018 edition).</li>
<li><a href="https://sites.ualberta.ca/~szepesva/papers/RLAlgsInMDPs.pdf">Reinforcement Learning Algorithms</a>. Csaba Szepesvari (2009).</li>
<li>Markov Decision Processes. Martin Puterman (1994).</li>
<li>Lecture notes of similar courses written by several other researchers: <a href="https://emiliekaufmann.github.io/RL.html">Emilie Kaufmann</a>, <a href="http://researchers.lille.inria.fr/munos/master-mva/index.html">Rémi Munos</a>, <a href="http://chercheurs.lille.inria.fr/~lazaric/Webpage/MVA-RL_Course15.html">Alessandro Lazaric</a> and <a href="https://perso.ens-lyon.fr/aurelien.garivier/www.math.univ-toulouse.fr/_agarivie/sites/default/files/MLDM_advancedML_StEtienne.pdf">Aurélien Garivier</a>.</li>
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