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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.
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Professional Training training in Dallas in July 2026 with Learni. Certified, expert trainers, eligible for employer funding. Free quote.
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Cybersecurity training in Sheffield in November 2026 with Learni. Certified, expert trainers, eligible for employer funding. Free quote.
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The Training: Mastering Ray RLlib - Complete Training on Distributed Reinforcement Learning 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 Training: Mastering Ray RLlib - Complete Training on Distributed Reinforcement Learning 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 Training: Mastering Ray RLlib - Complete Training on Distributed Reinforcement Learning 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.
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.
General presentation of Ray (key concepts, task distributor); reinforcement learning concepts (Markov Decision Process, policies, rewards); introduction to RLlib and overview of its main modules; getting started with OpenAI Gym compatible environments; installation and configuration of Ray and RLlib; first simple trainings in local mode.
Detailed presentation of supported algorithms (DQN, PPO, A3C, IMPALA, etc.); training parameters and tuning strategies; customization of environments and policies; introduction to creating custom models with TensorFlow or PyTorch; integration of callbacks for monitoring and logging; resource management and parallelism.
Distributed training on clusters (multi-node configuration, scaling); use of integrated monitoring tools (TensorBoard, Ray Dashboard); best practices for persistence, recovery, and model export; advanced integration into existing workflows via Ray Tune; failure management and troubleshooting; industrial application examples (finance, robotics, logistics); tips for going further.
Target audience
Engineers, researchers, data scientists, and developers wishing to design and deploy large-scale reinforcement learning algorithms
Prerequisites
Solid foundations in Python, fundamental knowledge in machine learning, and general knowledge of reinforcement learning
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