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- Triple validation: technical, pedagogical, sectoral.
- Minimum rating 4.8/5 over the last 12 sessions.
Training: Mastering Ray RLlib for Distributed Reinforcement Learning
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Learning objectives
- Understand the fundamental concepts of reinforcement learning (RL)
- Install, configure, and use Ray RLlib for distributed AI tasks
- Implement common RL algorithms with Ray RLlib
- Optimize policies and manage large-scale experiments
- Integrate RLlib with SimGym and custom environments
- Effectively supervise and debug distributed models
- Deploy RL agents in production
Program
Module 1Principles of Reinforcement Learning & Introduction to Ray
Basics of reinforcement learning, terminology (environment, agent, reward, episodes), exploration/exploitation; Introduction to Ray, fundamentals of distributed computing, installation of the Ray ecosystem (Ray, Ray Tune, Ray RLlib).
Module 2Getting Started with Ray RLlib & First Agents
Discovery of the RLlib architecture; definition and configuration of RLlib experiments; implementation of standard agents (DQN, PPO); use of pre-existing environments (Gym, Atari); observation of distributed execution, performance monitoring, and logs.
Module 3Customization, Scaling, and Advanced Integration
Creating custom environments; hyperparameter tuning with Ray Tune; scalability (multiple workers, CPU/GPU resources); management and saving of checkpoints; integration of RLlib policies into real applications; best practices for deployment and production.
Evaluation method
- Quiz to assess learning outcomes at the end of the training
- Daily practical exercises (programming missions, result analysis)
- Personalized review with feedback on final project
Learning method
- Interactive sessions with live coding
- Hands-on labs on cloud or local environments
- Industry case studies
- Access to notebooks and example scripts
Methods, materials and delivery
The Training: Mastering Ray RLlib for Distributed Reinforcement Learning program is delivered onsite or remote (blended-learning, virtual classroom, remote presence). At Learni, an industry-certified training organization, every program is built to maximize skills acquisition regardless of the chosen format.
The trainer alternates between demonstrative, interrogative and active methods (through hands-on labs and/or scenarios). This pedagogical approach guarantees concrete learning that's immediately applicable at work.
Equipment required
For the smooth delivery of the Training: Mastering Ray RLlib for Distributed Reinforcement Learning program, the following equipment is required:
- Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, projector or interactive touch screen (for remote sessions)
- Training environments installed on workstations or accessible online
- Course materials, hands-on exercises and complementary resources
- Post-training access to materials and educational resources
For intra-company training on a site outside Learni, the client commits to providing all required teaching materials (computers, internet, etc.) for the smooth delivery of the program in line with the prerequisites in the communicated program.
* contact us for remote delivery feasibility** ratio varies depending on the program
Skills assessment methods
Assessment of skills acquired during the Training: Mastering Ray RLlib for Distributed Reinforcement Learning program is performed through:
- During training: case studies, hands-on labs and professional scenarios
- End of training: self-assessment questionnaire and skills evaluation by the trainer
- After training: completion certificate detailing acquired skills
Program accessibility
Learni is committed to making its programs accessible. All our programs are accessible to people with disabilities. Our teams are available to adapt the pedagogical methods to your specific needs. Please contact us for any adjustment request.
Enrollment terms and lead times
Registration is possible up to 48 business hours before the start of training. All our programs are built for corporate L&D budgets and delivered onsite or remotely.
The Learni story
Founded by engineers and learning experts, Learni's mission is to make high-impact tech training accessible to teams everywhere. We work remotely with organizations across the US and Canada, in your time zone, to help teams upskill fast.
Our method
Training quality, guaranteed at every step
Before, during, after: we frame the brief, introduce the trainer, tailor the content and measure impact. You stay in control from kickoff to wrap-up.
You meet the trainer beforehand
30-minute video call between you and the selected trainer to validate the fit, adjust content and clear any final doubts.
- Live briefing on goals and team context.
- Veto right — we swap the trainer for free if needed.
Content tailored to your context
No recycled slides. The syllabus is reworked from your real cases: tools, constraints, vocabulary, ongoing projects.
- Hands-on cases drawn from your stack and projects.
- Program co-written then validated by your team.
Continuous quality follow-up
Live evaluations, 30/90/180-day check-ins and a consolidation plan. If the impact misses the mark, we rework it.
- NPS, knowledge quizzes and skills self-assessment.
- Satisfaction guarantee: fully satisfied or free rework.
A simple promise: you don't pay to discover the trainer on day one. Everything is validated upfront, by you.
Our social commitment
A school kit donated to a child for every training booked
To fight inequalities in access to education, Learni donates a complete school kit to a child in need for every training booked. You build your skills, a child heads back to school.
- Backpack, notebooks and essential supplies
- Distributed through our partner charities
- Included, at no extra cost to you
FAQ
Frequently asked questions
What can participants do after Training: Mastering Ray RLlib for Distributed Reinforcement Learning training?+
They can understand the fundamental concepts of reinforcement learning (RL), install, configure, and use Ray RLlib for distributed AI tasks and implement common RL algorithms with Ray RLlib. Those are the stated objectives of the Training: Mastering Ray RLlib for Distributed Reinforcement Learning program at Intermediate level, checked by the end-of-session assessment.
What does the Training: Mastering Ray RLlib for Distributed Reinforcement Learning program cover, day by day?+
Day 1: Principles of Reinforcement Learning & Introduction to Ray. Day 2: Getting Started with Ray RLlib & First Agents. Day 3: Customization, Scaling, and Advanced Integration. The Training: Mastering Ray RLlib for Distributed Reinforcement Learning program runs over 3 days, delivered on site.
How is Training: Mastering Ray RLlib for Distributed Reinforcement Learning training assessed?+
Assessment relies on quiz to assess learning outcomes at the end of the training, daily practical exercises (programming missions, result analysis) and personalized review with feedback on final project. Learni issues a completion certificate together with the individual assessment report at the end of the Training: Mastering Ray RLlib for Distributed Reinforcement Learning program, which an L&D team can file as evidence.
How much does Training: Mastering Ray RLlib for Distributed Reinforcement Learning training cost per participant?+
Training: Mastering Ray RLlib for Distributed Reinforcement Learning training is priced on quote. The amount depends on headcount, format and how far the syllabus is tailored to your team. A detailed quote is sent within one business day, and team pricing covers the whole group from the second participant.
How long does Training: Mastering Ray RLlib for Distributed Reinforcement Learning training take?+
3 days, available live online (US time zones) or on-site at your offices. Half-day tracks spread over several weeks are possible so the team stays operational.
Who pays for Training: Mastering Ray RLlib for Distributed Reinforcement Learning training?+
Most US teams pay directly through their company L&D or training budget. Learni invoices in US dollars and accepts bank transfer (ACH/wire) or card, with volume pricing for teams. A purchase order is welcome.
What do participants need to know before Training: Mastering Ray RLlib for Distributed Reinforcement Learning training?+
Basic knowledge of Python, machine learning, and fundamentals of reinforcement learning.
Is a certificate issued after Training: Mastering Ray RLlib for Distributed Reinforcement Learning training?+
Yes. A Learni completion certificate is issued, along with the individual evaluation report. Learni is a certified training provider and prepares for third-party certifications, but does not award them itself.
Does Learni provide the equipment for Training: Mastering Ray RLlib for Distributed Reinforcement Learning training?+
No. A computer and a stable internet connection are required for each participant. Learni provides the learning platform, the trainer and all course materials.
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