Loading...
Please wait a moment
Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
Which format do you prefer?
30 free minutes with a training advisor — no commitment.
Loading available slots...
Step-by-step guide to mastering digital project management skills through Learni's bootcamp launching in April 2026, including enrollment tips, curriculum details, and career prospects.
Explore the latest Power BI training options, essential Microsoft certifications like PL-300 and DP-600, and promising career trajectories for data professionals targeting April 2026.
Master competitive analysis skills essential for product teams with this step-by-step guide, including tools, frameworks, and 2026 trends like AI-driven insights.
Artificial Intelligence training in San Francisco in October 2026 with Learni. Certified, expert trainers, eligible for employer funding. Free quote.
Don't let this gap widen
Sans maîtrise de Ray RLlib, vos algorithmes d'apprentissage par renforcement peinent à scaler, limitant les déploiements à grande échelle et freinant l'innovation.
Les projets RL non distribués multiplient par 5 les temps d'entraînement, gaspillant jusqu'à 100 000 € en ressources GPU inutilisées par itération.
75 % des échecs en RL proviennent de problèmes de distribution, exposant votre entreprise à des pertes de parts de marché de 20 % annuelles et compromettant votre carrière.
Chaque mois sans expertise équivaut à un retard critique, laissant vos concurrents dominer le terrain.
The Maîtriser Ray RLlib : Formation complète à l'apprentissage par renforcement distribué training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Maîtriser Ray RLlib : Formation complète à l'apprentissage par renforcement distribué training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Maîtriser Ray RLlib : Formation complète à l'apprentissage par renforcement distribué training is carried out through:
Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Présentation générale de Ray (concepts clés, distributeur de tâches) ; notions d'apprentissage par renforcement (Markov Decision Process, politiques, récompenses) ; introduction à RLlib et survol de ses modules principaux ; prise en main des environnements compatibles OpenAI Gym ; installation et configuration de Ray et RLlib ; premiers entraînements simples en mode local.
Présentation détaillée des algorithmes pris en charge (DQN, PPO, A3C, IMPALA, etc.) ; paramètres d’entraînement et stratégies de tuning ; personnalisation des environnements et des politiques ; introduction à la création de modèles customisés avec TensorFlow ou PyTorch ; intégration de callback pour suivi et logging ; gestion des ressources et du parallélisme.
Entraînement distribué sur cluster (configuration multi-node, scaling) ; exploitation des outils de monitoring intégrés (TensorBoard, Ray Dashboard) ; bonnes pratiques pour la persistance, la récupération et l’export des modèles ; intégration avancée dans des workflows existants via Ray Tune ; gestion des échecs et troubleshooting ; exemples d’application industrielle (finance, robotique, logistique) ; conseils pour aller plus loin.
Target audience
Ingénieurs, chercheurs, data scientists et développeurs souhaitant concevoir et déployer des algorithmes d’apprentissage par renforcement à grande échelle
Prerequisites
Bases solides en Python, notions fondamentales en apprentissage automatique et connaissance générale sur l’apprentissage par renforcement
Loading...
Please wait a moment






























Discover a step-by-step roadmap to become a skilled AI engineer by March 2026. From prerequisites to advanced projects, tools, and job strategies, this guide covers everything for aspiring professionals.
Software Development training in Louisville in January 2025 with Learni. Certified, expert trainers, eligible for employer funding. Free quote.