Reinforcement Learning Applications
Some problems are about sequences of decisions rather than single predictions, which is where reinforcement learning fits and where it is usually misapplied. This course covers when it is the right tool.
Course outline
What this programme covers, module by module.
- 01Sequential decisions and where this differs from prediction
- 02States, actions, rewards and the environment
- 03Value based and policy based approaches
- 04Reward design and the ways it misfires
- 05Simulation, sample efficiency and training cost
- 06Safe evaluation before touching a real system
- 07When simpler methods are the better answer
What you will gain
- Recognise problems suited to sequential decision learning
- Frame a problem as states, actions and rewards
- Explain the main learning approaches
- Design reward functions that do not misfire
- Evaluate a policy safely before deployment
- Recognise where simpler methods would do better
Who should attend
- Data scientists and machine learning engineers
- Operations research analysts
- Control and automation engineers
- Technical staff evaluating advanced methods
Upcoming sessions
| Date | City | Duration | Fees | Book |
|---|---|---|---|---|
| 9 to 13 Nov 2026 | Pretoria | 5 Days | R18,000 | |
| 23 to 27 Nov 2026 | Durban | 5 Days | R18,000 | |
| 11 to 15 Jan 2027 | Cape Town | 5 Days | R18,000 | |
| 1 to 5 Feb 2027 | Johannesburg | 5 Days | R18,000 | |
| 5 to 9 Apr 2027 | London | 5 Days | US$1,800 | |
| 21 to 25 Jun 2027 | Dubai | 5 Days | US$1,800 |
Fees are per delegate and exclude VAT. Public intakes are confirmed once minimum numbers are met. We also deliver this course on your own schedule, in-house or live online.
Enquire about this course
Register your team or ask for a tailored in-house quote. We reply within one working day.
WhatsApp usCourse brochure (PDF)- Fees from
- R18,000 per delegate
- Duration
- 5 Days
- Field
- Artificial Intelligence & Emerging Technology
- Formats
- In-house, public, online
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