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<title>Sequential Learning</title>
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<h1 id="logo"><a href="#">Sequential Learning (2022-2023)</a></h1>
<p>Rémy Degenne</p>
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<h3>Sequential learning</h3>
Most of the course is adapted from the course given last year by Emilie Kaufmann (see <a href="https://emiliekaufmann.github.io/RL.html">her page</a>).
</br>
For information about projects, see <b><a href="https://remydegenne.github.io/SL_2022_projects">here</a></b>
<ul>
<li><b>Lecture 1</b> - <a href="https://remydegenne.github.io/docs/SL_2022/Cours1.pdf">Reinforcement learning</a></li>
<li><b>Lecture 2</b> - <a href="https://remydegenne.github.io/docs/SL_2022/Cours2.pdf">Dynamic programming</a></li>
<li><b>Lecture 3</b> - <a href="https://remydegenne.github.io/docs/SL_2022/Cours3.pdf">Reinforcement learning algorithms</a></li>
<li><b>Practical Session 1</b> - <a href="https://remydegenne.github.io/docs/SL_2022/readme.txt">readme</a> and <a href="https://remydegenne.github.io/docs/SL_2022/ValueIteration_and_QLearning.ipynb">notebook</a></li>
<li><b>Lecture 4</b> - <a href="https://remydegenne.github.io/docs/SL_2022/Cours4.pdf">Reinforcement learning with function approximation</a></li>
<li><b>Lecture 4.5</b> - <a href="https://remydegenne.github.io/docs/SL_2022/Cours4demi.pdf">Summary of some points from the first 4 courses</a></li>
<li><b>Lecture 5</b> - <a href="https://remydegenne.github.io/docs/SL_2022/Cours5.pdf">Beyond Value-Based Methods</a></li>
<li><b>Lecture 6</b> - <a href="https://remydegenne.github.io/docs/SL_2022/Cours6.pdf">Stochastic Multi-Armed Bandits</a></li>
<li><b>Lecture 7</b> - <a href="https://remydegenne.github.io/docs/SL_2022/Cours7.pdf">Structured Bandits</a></li>
<li><b>Practical Session 2</b> - <a href="https://remydegenne.github.io/docs/SL_2022/readme_tp2.txt">readme</a> and <a href="https://remydegenne.github.io/docs/SL_2022/bandits.ipynb">notebook</a></li>
<li><b>Lecture 8</b> - <a href="https://remydegenne.github.io/docs/SL_2022/Cours8.pdf">Bandit Identification</a></li>
<li><b>Practical Session 3</b> - <a href="https://remydegenne.github.io/docs/SL_2022/readme_tp3.txt">readme</a> and <a href="https://remydegenne.github.io/docs/SL_2022/DeepQLearning.ipynb">notebook</a></li>
<li><b>Lecture 9</b> - <a href="https://remydegenne.github.io/docs/SL_2022/Cours9.pdf">Exploration in RL</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 colleagues: <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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