Reinforcement Learning Masterclass
A 24-hour reinforcement-learning masterclass — from Markov decision processes and Q-learning to deep Q-networks, trained in OpenAI Gym and the CARLA self-driving simulator. You learn how agents make sequential decisions and how to train them safely in simulation.
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Course Info
- Type
- Certification
- Subject
- Individual & Professional Certification
- Duration
- 24 hours
- Course code
- RLM
- Prerequisites
- Basic computer literacy; no advanced prerequisites.
Delivery
- Live-virtual — Instructor-led online cohorts.
- On-site — In-person at your premises or ours.
Tools
- MDP
- Q-learning
- DQN
- OpenAI Gym
- CARLA
- self-driving
What you'll learn
A 24-hour reinforcement-learning masterclass — from Markov decision processes and Q-learning to deep Q-networks, trained in OpenAI Gym and the CARLA self-driving simulator. You learn how agents make sequential decisions and how to train them safely in simulation.
From Markov decision processes and Q-learning to deep Q-networks, you train agents in simulated environments and apply them to control and autonomous-driving scenarios. The capstone trains a control or driving agent.
- ML practitioners exploring decision-making and control.
- Robotics, autonomous-systems and simulation engineers.
- Model decision problems as Markov decision processes.
- Train agents with Q-learning and deep Q-networks.
- Use OpenAI Gym to build and test environments.
- Apply RL to control and self-driving with CARLA.
- Evaluate agent policies and reward design.
- Hands-on labs throughout the program (70% project-based).
- A capstone project demonstrating end-to-end skills.
- Train agents with Q-learning and DQN in simulated environments.
- Apply RL to control and autonomous-driving scenarios.
Globally accredited certificate issued by Epsilon AI Learning — USA, with a unique Certificate ID and EPSILON ID. Awarded on 80% attendance, 80% final exam, and a capstone project.
Program Curriculum
- Agents, environments and rewards
- Exploration vs. exploitation
- Markov decision processes (MDP)
- Q-learning and value iteration
- Policies and the Bellman equation
- Tuning learning parameters
- Function approximation with DQN
- Experience replay and target networks
- Stability and common pitfalls
- Building environments in OpenAI Gym
- Reward shaping
- Evaluating agent policies
- The CARLA simulator
- Control and driving tasks
- Safety in simulation
- Defining a control problem
- Training and evaluating the agent
- Analyzing behavior
Frequently asked questions
What should I know first?
It is recommended after CDSP, DLM, CGAIP and the Classic CV Masterclass (CCVCM) — it is the final step of the Computer Vision Engineer path. With those foundations in place, the program builds RL concepts from the ground up.
Where do I train the agents?
In simulated environments — OpenAI Gym for control tasks and the CARLA simulator for self-driving scenarios.
What algorithms are covered?
Markov decision processes, Q-learning and deep Q-networks (DQN), applied to control and autonomous-driving problems.
How much does the program cost?
We quote on a call rather than publishing a figure, because the price depends on the format you pick — live online, onsite, or a private cohort for a team — and because we would rather check the programme is right for you before talking about money. An advisor goes through what it covers, what you will be able to do at the end, and the prerequisites, and then gives you the exact price and the payment options. It takes about twenty minutes and there is no obligation. Request it from the form on this page.
When does the next cohort start?
New cohorts open regularly, online and onsite, and we confirm dates on the same call as the price — partly because they move, and partly because the right date depends on which format suits you and whether you meet the prerequisites yet. Ask on the form and an advisor will give you the next available starts that fit your schedule.
Is the program online or onsite, and in which language?
Both — Epsilon runs live, instructor-led sessions online and onsite, plus dedicated corporate cohorts. Programs are delivered in English and Arabic, with bilingual materials and instructor support. Tell us your preference in the registration form.
What certificate will I receive?
A globally accredited certificate from Epsilon AI Learning — USA, carrying a unique Certificate ID and Epsilon ID. It is awarded on 80% attendance, an 80% final exam, and a completed capstone project, and it is verifiable online.
Continue your pathway
This program is part of the Epsilon certification framework. Explore the full seven-level pathway and top-up programs to build on what you learn here.
Computer Vision Engineer
- Certified Data Scientist Professional
- Deep Learning Masterclass
- Certified Generative AI Professional
- Classic CV Masterclass
- Reinforcement Learning Masterclass
After 2 image-recognition apps
Led by Epsilon's expert instructors
Every program is delivered by practitioners who ship AI and analytics in industry — the same team across all Epsilon programs.
Where our graduates work
A sample of the employers hiring Epsilon Learning graduates across the region and beyond.
Earning your accredited certificate
To receive the accredited certificate you need at least 80% attendance, at least 80% in the final exam, and a completed capstone project.
When the next cohort starts
Dates are confirmed on the same call as the price, because they move and because the right start depends on the format that suits you. Ask to be told when the next one opens and an advisor will come back with the starts that fit your schedule.
Tell me when the next one opens
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