AI developer, 1 day course

Reinforcement Learning and Autonomous Systems course
in Cardiff, in-house or online.

Train intelligent agents with Q-learning, policy gradients and actor-critic methods, then deploy them.

Duration
1 day
Price
£195 per person
Next public date
5 October 2026
Delivery
Cardiff, in-house or online
Book this course
£195per person

1 day, trainer-led and hands-on

Choose a public date
Request a place → Book a private course for your team
  • Run in-house at your workplace
  • Also available live online
  • Hands-on exercises throughout
// About this course

Reinforcement Learning and Autonomous Systems in practice.

This course provides an in-depth, practical introduction to reinforcement learning (RL) and autonomous AI systems. Learners explore how AI agents make decisions, optimise actions, and learn from feedback in dynamic environments. The course combines theory, hands-on coding, and real-world projects, covering algorithms such as Q-learning, policy gradients, and actor-critic methods. Participants gain experience designing RL agents, simulating environments, and applying autonomous decision-making to robotics, finance, gaming, and other real-world scenarios. By the end, learners will have the skills to implement intelligent, adaptive systems and develop advanced AI applications.

Who this course is for

This course is designed for AI developers, software engineers, data scientists, and technically-minded professionals seeking to specialise in reinforcement learning and autonomous systems. It is also suitable for students and career changers interested in building intelligent agents and adaptive AI solutions.

Reinforcement learning agent in simulation
// Course outline

What the course covers.

  1. Module 1

    Foundations of Reinforcement Learning

    Learners begin by understanding the core concepts of RL, including agents, environments, rewards, states, and actions. This module covers the theory behind Markov Decision Processes (MDPs), exploration vs exploitation, and value-based methods such as Q-learning. Hands-on exercises allow learners to implement basic RL algorithms in Python and observe agent behaviour in simulated environments.

  2. Module 2

    Advanced Algorithms & Techniques

    This module introduces advanced reinforcement learning methods, including policy gradients, actor-critic algorithms, and deep reinforcement learning using neural networks. Learners work with frameworks such as TensorFlow, PyTorch, and OpenAI Gym to train agents in complex environments, gaining practical experience in algorithm selection, tuning, and performance evaluation.

  3. Module 3

    Autonomous Systems & Real-World Applications

    Learners apply reinforcement learning to build autonomous systems capable of adaptive decision-making. The module covers integration with robotics, simulation environments, and AI-driven decision systems. Participants also learn best practices for deploying RL agents, managing safety and reliability, and scaling systems for real-world applications in industries like robotics, gaming, finance, and aerospace.

// Why attend

Why teams take this course.

Attendees gain practical skills to design, implement, and deploy reinforcement learning agents in real-world scenarios. The hands-on approach ensures learners understand both the theory and application of RL, empowering them to develop autonomous systems capable of decision-making in dynamic environments.

This reinforcement learning course was incredible. The combination of theory and hands-on coding made complex concepts easy to understand. Building autonomous agents in simulation environments was challenging but highly rewarding. I now feel confident applying RL in real projects.

Course delegate
// In-house training

Run this course for your team

We can deliver Reinforcement Learning and Autonomous Systems at your workplace, or live online. Private courses use examples from your own work, run on dates that suit you, and every delegate gets a course pack. There are no hidden costs.

Ask about an in-house course →
In-house training delivered at a client's workplace
// Common questions

Frequently asked questions.

How long is the course and how much does it cost?
The Reinforcement Learning and Autonomous Systems course runs for one day. Public course places are £195 per person. Private courses for your team are quoted on request.
Do I need any previous experience?
This is a technical course for developers, engineers and technical professionals. If you are unsure whether it suits your experience, contact us before booking and we will advise.
Can you run this course for our team at our workplace?
Yes. We deliver this course in-house at your offices in Cardiff, across Wales and throughout the UK, for groups from one to over fifty people, with examples tailored to your work.
Is the course available online?
Yes. We can run it as a live, trainer-led online session, so teams across different sites can take part together.
How do I book a place?
Choose a date and select Request a place, or contact us. We will confirm availability and send joining details.
// Get started

Tell us what your team needs to learn.

A short call is enough for us to recommend a course or put together a programme and a quote.

Book a consultation → info@apextrainingwales.co.uk