Diploma in Classical and Deep RL for Dynamic Systems
Online postgraduate diploma of 800 hours in classical and deep reinforcement learning for dynamic systems. 8 modules covering Markov decision processes, tabular methods, function approximation, value-based deep RL, policy gradient and actor-critic, model-based RL and sim-to-real transfer in line with ISO/IEC 22989:2022, ISO/IEC 23053:2022 and IEC 61508:2010, with an assigned technical tutor.
Diploma in Classical and Deep RL for Dynamic Systems — studying from Canada
Canada is a recognised centre for artificial intelligence research, with hubs in Toronto, Montréal and Edmonton supported by institutes such as the Vector Institute, Mila and Amii. Engineering teams across manufacturing, energy, finance and transport apply simulation, machine learning and data analytics to real operational problems. The federal government's Pan-Canadian AI Strategy and privacy regulation shape how organisations handle data. Typical employers include technology firms, banks, aerospace and automotive companies, and public research bodies.
Fees for Canada: C$2,010 (Canadian dollar), with instalments and employer sponsorship available.
Relevant bodies in Canada
Typical roles
Questions from Canada
Do Canadian bodies recognise data or AI qualifications from private institutes?
Data and AI roles in Canada are generally not licensed in the way engineering is, so recognition depends on employers and professional communities rather than a regulator. Engineering licensing through provincial associations applies only to engineering titles. SEIUM's qualifications are private, not official state degrees and not accredited by a national body, so employers decide how much weight to give them.
Do Canadian employers hire for data and AI skills, and would this help?
Canada has a large and active data and AI sector, with notable hubs in Toronto, Montreal and Vancouver, and employers hire widely for these skills. They usually look first at portfolios, technical interviews and proven projects. A SEIUM final project can demonstrate your ability, but a private qualification's value is decided by each employer, so present it alongside concrete work.
Is this manageable with a job, and what level of English is needed?
Because the programmes are fully online, you can study from home around your job, and no student visa is needed. Specialist courses are 300 hours, postgraduate diplomas 800 and master's 1,900, so plan your weekly time. Teaching is in English, so strong written and reading English is important. Instalment payments and possible employer sponsorship can help with funding.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Model dynamic systems as Markov decision processes.
Implement tabular temporal-difference and Monte Carlo methods.
Apply function approximation and deep networks to value learning.
Train actor-critic agents in continuous action spaces.
Integrate learned dynamic models into planning and control.
Design simulation-to-real transfer strategies.
Evaluate agents with reproducible metrics and safety criteria.
Who it's for
- Reinforcement learning engineers.
- Intelligent control engineers looking beyond classical PID and MPC.
- Robotics researchers.
- Data scientists working in optimisation.
- Autonomous systems engineers.
- Simulation developers.
- Engineering or science graduates seeking to enter industrial robotics.
What you'll work with
- ISO/IEC 22989:2022 (AI concepts and terminology)
- ISO/IEC 23053:2022 (Framework for AI systems using machine learning)
- IEC 61508:2010 (Functional safety of E/E/PE safety-related systems)
- ISO 26262:2018 (Road vehicles: functional safety)
- Regulation (EU) 2024/1689 (EU Artificial Intelligence Act)
- ISO/IEC 23894:2023 (AI risk management)
- IEC 61131-3:2013 (PLC programming languages)
- Gymnasium
- Stable-Baselines3
- MuJoCo
- Ray RLlib
- NVIDIA Isaac Lab
8 modules
1.1States, actions, rewards and transitions1.2Discounted return and horizon1.3Bellman equations for value and action-value1.4Value iteration and policy iteration1.5Dynamic programming and contraction1.6Partially observable MDPs and belief states1.7Discretisation of continuous dynamic systems1.8Reward function design1.9Relationship with LQR optimal control1.10Workshop: inverted pendulum solved by dynamic programming
2.1Monte Carlo policy evaluation2.2Temporal differences TD(0) and TD(lambda)2.3Eligibility traces2.4SARSA and on-policy control2.5Q-learning and off-policy control2.6Epsilon-greedy and softmax exploration2.7Double Q-learning and maximisation bias2.8Dyna-Q and model-based planning2.9Convergence and Robbins-Monro conditions2.10Lab: Q-learning on discretised MountainCar
3.1Linear approximation and basis functions3.2Tile coding and Fourier bases3.3Semi-gradient TD with linear approximation3.4The deadly triad of instability3.5Least-squares TD and LSPI3.6Neural networks as approximators3.7Normalisation of observations and rewards3.8Generalisation across states3.9Hyperparameter tuning and seeds3.10Practical exercise: CartPole control with linear approximation
4.1Deep Q-Network and experience replay4.2Target networks and stabilisation4.3Double DQN and Dueling DQN4.4Prioritised experience replay4.5Rainbow and combining improvements4.6Distributional RL: C51 and QR-DQN4.7Exploration with parametric noise4.8Discrete actions in physical systems4.9Diagnosing learning curves4.10Lab: DQN with Stable-Baselines3
5.1Policy gradient theorem and REINFORCE5.2Baselines and variance reduction5.3Actor-critic A2C and A3C5.4Generalised advantage estimation (GAE)5.5TRPO and trust regions5.6PPO with ratio clipping5.7DDPG for continuous actions5.8TD3 and critic overestimation5.9Soft Actor-Critic and maximum entropy5.10Lab: SAC on a robotic arm in MuJoCo
6.1Learning dynamic models with neural networks6.2PETS probabilistic ensembles6.3Model predictive control with a learned model6.4MBPO and short rollouts6.5World models and Dreamer6.6System identification versus learned models6.7Compounding error and planning horizon6.8Sample efficiency in real systems6.9Combining physics and data6.10Practical exercise: quadrotor control with a learned model
7.1The reality gap in dynamics and sensors7.2Domain randomisation of physical parameters7.3Parameter identification and calibration7.4Offline RL with CQL and IQL7.5Safety when deploying on hardware7.6Delays, noise and actuator saturation7.7Gymnasium and Isaac Lab environments7.8Evaluation and reproducible benchmarks7.9AI regulation and functional safety7.10Case study: temperature control of a thermal process
8.1System selection and MDP formulation8.2Building the simulation environment8.3Reward and constraint design8.4Training baseline agents8.5Comparison of algorithms and seeds8.6Robustness analysis against disturbances8.7Hardware transfer plan8.8Reproducible documentation of the experiment8.9Report and presentation8.10Final project: final review with the tutor
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme is compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Gymnasium, Stable-Baselines3, MuJoCo, Ray RLlib and NVIDIA Isaac Lab, in free, academic or evaluation versions. Practical work is based on real procedures for control of pendulums and underactuated systems in simulation, tuning of thermal process controllers and simulated robotic locomotion and manipulation. Assessment is continuous by module plus the final project.
Standards covered: ISO/IEC 22989:2022, ISO/IEC 23053:2022, IEC 61508:2010, ISO 26262:2018, Regulation (EU) 2024/1689, ISO/IEC 23894:2023, IEC 61131-3:2013.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer capable of running the programme software.
- English sufficient to read technical standards.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with Gymnasium, Stable-Baselines3, MuJoCo, Ray RLlib and NVIDIA Isaac Lab, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute diploma, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
Prior technical training or professional experience in the sector (no university degree required), a computer able to run the programme software and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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