Reinforcement Learning Fundamentals Training in Ontario

Reinforcement Learning Fundamentals Training in Ontario, Canada

Learn the basics of reinforcement learning and how agents can make decisions through trial and error.

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Reinforcement Learning Fundamentals Training is a professional training program delivered by ProgNXT in Ontario, Canada. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this Reinforcement Learning Fundamentals Training course with hands-on, instructor-led sessions. The Reinforcement Learning Fundamentals Training provides a comprehensive introduction to the core concepts, techniques, and applications of...

Expert Panel

Designed by the ProgNXT AI & Data Science Expert Panel, specializing in Generative AI, Machine Learning, and ChatGPT applications

ProgNXT AI & Data Science Expert Panel

Course Code: PRQ35
14 Hrs
Last Updated: April 2026

Course Rating5/5

The Reinforcement Learning Fundamentals Training provides a comprehensive introduction to the core concepts, techniques, and applications of reinforcement learning (RL). Participants will explore the principles of agent-based learning, reward systems, and policy optimization, gaining practical knowledge to design, implement, and evaluate RL models. This training emphasizes hands-on learning, enabling participants to apply RL concepts to real-world problems using popular frameworks and tools.

Welcome to the official Reinforcement Learning Fundamentals Training certification program in Ontario, Canada. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge locally. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 14 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

Basic understanding of Python programming
Familiarity with machine learning concepts and algorithms
Knowledge of linear algebra and probability (helpful but not mandatory)
Basic understanding of neural networks (optional but beneficial)

Upon completing the Reinforcement Learning for Beginners course, participants will:

Gain a foundational understanding of reinforcement learning (RL) concepts.
Learn how to design and implement simple RL algorithms using Python.
Understand how agents interact with environments, learn from feedback, and optimize actions over time.
Be able to apply RL techniques to simple problems and real-world applications.

What Skills It Will Add

Reinforcement Learning Concepts: Understanding the basic principles of RL, including agents, environments, rewards, and policies.
RL Algorithms: Ability to implement fundamental RL algorithms such as Q-learning and SARSA.
Exploration vs. Exploitation: Knowledge of balancing exploration and exploitation in RL problems.
Policy Optimization: Skills in optimizing policies to maximize rewards in different environments.
Practical RL Application: Ability to apply RL techniques to simple environments and problems using Python and relevant libraries.

Participants will be assessed on:

RL Basics: Understanding the core components of reinforcement learning, including agents, environments, and rewards.
Q-learning: Ability to implement the Q-learning algorithm for model-free reinforcement learning.
Exploration and Exploitation: Knowledge of epsilon-greedy policies and other exploration strategies.
Policy Optimization: Proficiency in optimizing policies for maximum reward accumulation.
Practical Exercises: Ability to solve basic RL problems using Python libraries and custom implementations.

Certificate of Completion: Participants who attend all sessions and successfully complete the course assessments will receive a ProgNXT Certificate of Completion for the Reinforcement Learning for Beginners Training Program.

Introduction to Reinforcement Learning and Basic Algorithms

Introduction to Reinforcement Learning

Overview of reinforcement learning and its applications
Key concepts: Agent, Environment, Reward, Policy
The Markov Decision Process (MDP)
Practical exercise: Understanding RL environments and agents using simple examples

Types of Reinforcement Learning

Model-free vs. model-based learning
On-policy vs. off-policy learning
Exploration vs. exploitation dilemma
Practical exercise: Implementing an epsilon-greedy policy

Q-learning Algorithm

Understanding Q-values and Q-tables
Introduction to Bellman equations
Implementing Q-learning in a simple grid world environment
Practical exercise

Introduction to Deep Q Networks (DQN)

Overview of Deep Q Networks and their importance in RL
Comparing traditional Q-learning with DQN
Practical exercise

Advanced Concepts and Practical Applications

SARSA Algorithm

Understanding SARSA (State-Action-Reward-State-Action)
Differences between Q-learning and SARSA
Practical exercise: Implementing SARSA in a grid world environment

Policy Optimization

Understanding policy iteration and value iteration
Implementing a simple policy optimization technique
Practical exercise: Implementing policy gradient methods

Applications of Reinforcement Learning

Real-world applications of RL: Game playing, robotics, and autonomous systems
RL in gaming: Example with OpenAI Gym
Practical exercise

Challenges in Reinforcement Learning

Delayed rewards and exploration issues
Stability and convergence of RL algorithms
Practical exercise

Course Recap and Q&A Session

Review of key concepts learned throughout the course
Open floor for questions and discussion
Final thoughts and next steps for further learning

Course slides and reference materials
Sample code for key algorithms like Q-learning and SARSA
Access to Python notebooks for hands-on exercises and practice
Pre-configured environments for experimenting with RL problems

Participants will be evaluated through hands-on exercises, quizzes, and a final project.
ProgNXT Certificates of completion will be awarded to participants who successfully complete the course assessments.

Reinforcement Learning Fundamentals Training Events in ontario

Online Events

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More Upcoming Session Dates

October 2026

📍 Ontario, Canada

19th Oct 2026→20th Oct 2026

🌐 Online Instructor-Led

21st Oct 2026→22nd Oct 2026

November 2026

📍 Ontario, Canada

3rd Nov 2026→4th Nov 2026

🌐 Online Instructor-Led

4th Nov 2026→5th Nov 2026

📍 Ontario, Canada

24th Nov 2026→25th Nov 2026

🌐 Online Instructor-Led

26th Nov 2026→27th Nov 2026

December 2026

🌐 Online Instructor-Led

1st Dec 2026→2nd Dec 2026

📍 Ontario, Canada

12th Dec 2026→13th Dec 2026

📍 Ontario, Canada

25th Dec 2026→26th Dec 2026

🌐 Online Instructor-Led

27th Dec 2026→28th Dec 2026

Events for other courses in Ontario, Canada

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Reinforcement Learning Fundamentals Training in Ontario