Training: Mastering Ray RLlib - Developing Large-Scale Reinforcement Learning Agents

On quote$2,310

  • 2 days
  • Onsite
  • 8 people max.
Ref: EQB684
8 people max.
On quote$2,310
In-company rate: on quotefrom $4,190 for the whole team
On-site and certification exam on quote
2 days
Onsite

Don't let this gap widen

Why this program matters

  • Without Ray RLlib mastery, reinforcement learning agents stay confined to small scale, multiplying training times by 10 and wasting up to 500 000 € in cloud resources per project.

  • 75 % of production RL deployments fail due to inadequate scalability, resulting in estimated annual revenue losses of 2 millions d'euros for lagging companies.

  • Your team loses competitiveness against optimized rivals while your career stalls in a market where 80 % of senior positions require distributed RL expertise.

  • Each quarter without upskilling risks missing critical opportunities in industrial AI.

Allan BUSI, Learni trainer · AI expert
Allan BUSI

Learni trainer · AI expert

73%productivity gap
×3cost of inaction
Flag of the United StatesBuilt for US teams
Payment by wire transferEnhanced confidentialityPerfect for companies with 50 to 500 employeesScheduling to fit any time zone

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Companies: Aptar, ArcelorMittal, Equans, EDF, Ubisoft and Microsoft.

Learning objectives

  • Understand the key concepts of reinforcement learning and the Ray RLlib framework
  • Install, configure, and get hands-on with Ray RLlib
  • Train, evaluate, and optimize RL agents with RLlib
  • Experiment on complex and custom environments
  • Deploy large-scale distributed RL solutions

Program

Module 1Fundamentals of Ray and Introduction to RLlib

Quick overview of reinforcement learning and Ray; Basic concepts: policies, environments, rewards; Introduction to Ray, Ray Tune, and Ray RLlib; Installation and configuration; Using standard OpenAI Gym environments with RLlib

Module 2Implementation and Optimization of RL Agents

Understand supported algorithms (DQN, PPO, A3C...); Launching trainings, monitoring, and evaluation; Hyperparameter fine-tuning via Ray Tune; Creating custom environments; Using observers and callbacks

Module 3Ray RLlib at Scale: Practical Deployment Cases

Distributed training across multiple machines; Resource management, scalability, and optimization; Exporting and deploying agents, integration into industrial pipelines; Monitoring, saving, and reusing models; Case studies and concrete scenarios

Evaluation method

  • Red thread project on end-to-end RL agent design
  • Multiple-choice quizzes at the end of each module
  • Continuous assessment with practical exercises

Learning method

  • Alternating theoretical presentations and practical workshops on notebooks
  • Real-world case studies from industry
  • Detailed training materials provided to participants

Methods, materials and delivery

The Training: Mastering Ray RLlib - Developing Large-Scale Reinforcement Learning Agents 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 - Developing Large-Scale Reinforcement Learning Agents 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 - Developing Large-Scale Reinforcement Learning Agents 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.

Step 1

Rigorous trainer selection

Each trainer is validated on three criteria: hands-on field expertise, proven pedagogy and alignment with your industry.

  • Triple validation: technical, pedagogical, sectoral.
  • Minimum rating 4.8/5 over the last 12 sessions.
Step 2

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.
Step 3

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.
Step 4

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.

A child walking to school with a backpack
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
Enroll and offer a kit
FAQ

Frequently asked questions

What can participants do after Training: Mastering Ray RLlib training?+
They can understand the key concepts of reinforcement learning and the Ray RLlib framework, install, configure, and get hands-on with Ray RLlib and train, evaluate, and optimize RL agents with RLlib. Those are the stated objectives of the Training: Mastering Ray RLlib program at Advanced level, checked by the end-of-session assessment.
What does the Training: Mastering Ray RLlib program cover, day by day?+
Day 1: Fundamentals of Ray and Introduction to RLlib. Day 2: Implementation and Optimization of RL Agents. Day 3: Ray RLlib at Scale: Practical Deployment Cases. The Training: Mastering Ray RLlib program runs over 2 days, delivered on site.
How is Training: Mastering Ray RLlib training assessed?+
Assessment relies on red thread project on end-to-end RL agent design, multiple-choice quizzes at the end of each module and continuous assessment with practical exercises. Learni issues a completion certificate together with the individual assessment report at the end of the Training: Mastering Ray RLlib program, which an L&D team can file as evidence.
How much does Training: Mastering Ray RLlib training cost per participant?+
Training: Mastering Ray RLlib 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 training take?+
2 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 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 training?+
Knowledge of Python programming, basics in machine learning and reinforcement learning.
Is a certificate issued after Training: Mastering Ray RLlib 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 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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