March 2025
Intermediate to advanced
214 pages
5h 26m
French
Content preview from Apprentissage par renforcement pour la finance
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Partie I. Les bases
La première partie du livre couvre les bases de l'apprentissage par renforcement et fournit des informations générales. Elle se compose de trois chapitres :
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Le chapitre 1 se concentre sur l'apprentissage par interaction avec quatre exemples majeurs : l'appariement des probabilités, la mise à jour bayésienne, l'apprentissage par renforcement (RL) et l'apprentissage Q profond (DQL).
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Le chapitre 2 présente les concepts de la programmation dynamique (PD) et discute du DQL en tant qu'approche des solutions approximatives aux problèmes de PD. Le thème principal est la dérivation de politiques optimales pour maximiser une fonction objective donnée en prenant une séquence d'actions et en mettant à jour la politique optimale de manière itérative. Le DQL est illustré à l'aide du jeu CartPole du progiciel Python Gymnasium.
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Le chapitre 3 développe un premier environnement
Financequi permet à l'agent DQL du chapitre 2 d'apprendre un jeu de prédiction financière. Bien que l'environnement reproduise formellement l'API de CartPole, il lui manque certaines caractéristiques importantes qui sont nécessaires pour appliquer la RL avec succès.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.