Q-learning Courses and Certifications

Q-Learning Unleashed: Building Intelligent Agents

Build a Q-learning agent from Bellman updates and Q-tables, add ε-greedy exploration, and use the learned policy for decision making.

• CodeSignal
• 1 hour
• Self-Paced
• Free Certificate

Q Learning in Reinforcement Training Basics

Learn Q-Learning fundamentals — Q-values, rewards, episodes, temporal difference, exploration vs. exploitation — and implement the algorithm in guided demos to train agents for robotics and gaming.

• Coursera
• 2 weeks, 1 hour a week
• Self-Paced
• Paid Course

University of Alberta;  Alberta Machine Intelligence Institute

Sample-based Learning Methods

44 ratings

Learn near-optimal policies from trial-and-error experience: implement Monte Carlo prediction and control, TD learning, Expected Sarsa, Q-learning, and the Dyna model-based architecture.

• Coursera
• 5 weeks, 4 hours a week
• Self-Paced
• Paid Course

Steve Brunton

Q-Learning - Model Free Reinforcement Learning and Temporal Difference Learning

Learn how model-free, gradient-free reinforcement learning leads from Monte Carlo and temporal-difference methods to off-policy Q-learning.

• YouTube
• 36 minutes
• Self-Paced
• Free Video

Jeff Heaton

Introduction to Q-Learning for Game Play - Lecture 12.2

Explore Q-learning in Python by building a policy table that selects actions for an environment, using a Mountain Car example and visualizations.

• YouTube
• 24 minutes
• Self-Paced
• Free Video

Game On: Integrating RL Agents with Environments

Integrates a Q-learning agent with a grid-world environment, balancing exploration and exploitation while visualizing rewards, policies, and value functions.

• CodeSignal
• 1 hour
• Self-Paced
• Free Certificate

University of Alberta;  Alberta Machine Intelligence Institute

Prediction and Control with Function Approximation

24 ratings

Scale reinforcement learning to infinite state spaces: implement TD, expected Sarsa, Q-learning and Actor-Critic with tile-coding and neural network function approximation.

• Coursera
• 5 weeks, 4 hours a week
• Self-Paced
• Paid Course

Reinforcement Learning

3 ratings

Learn Q-learning fundamentals and build, train, and test a Deep Q-Network reinforcement learning agent.

• YouTube
• 3 hours 30 minutes
• Self-Paced
• Free Video

Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2)

1112 ratings at Udemy

How to Turn Deep Reinforcement Learning Research Papers Into Agents That Beat Classic Atari Games

• Udemy
• 6 hours 45 minutes
• Self-Paced
• Paid Course

Playing Games with Reinforcement Learning

Learn the fundamentals of reinforcement learning by building a grid-world environment and training a Q-learning agent to navigate it. Explore agent-environment interaction, reward design, and explorat

• edX
• 2 weeks, 2-4 hours a week
• Self-Paced
• Free Online Course (Audit)

Reinforcement Learning with Gymnasium in Python

764 ratings at DataCamp

Discover the World of Reinforcement Learning Embark on an exhilarating exploration of Reinforcement Learning (RL), a pivotal branch of machine learning.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Deep Reinforcement Learning - Tutorial Session: Review of Q-Learning

Dive into Q-learning fundamentals with MDP review, parametric approaches, and practical algorithm walkthroughs for deep reinforcement learning mastery.

• YouTube
• 51 minutes
• Self-Paced
• Free Video

Reinforcement Learning

Build, train, backtest, and optimize a Q-learning trading strategy with Python, using market indicators and financial state and action spaces.

• Udacity
• 23 hours
• Self-Paced
• Paid Course

Value-Based Methods

Apply deep learning architectures to reinforcement learning tasks. Train your own agent that navigates a virtual world from sensory data.

• Udacity
• 10 hours
• Self-Paced
• Paid Course

Code With Aarohi

Learning Agents Using Q-Learning: Theory and Code - Agentic AI Course Lecture 13

Dive into Q-Learning for reinforcement learning with both theoretical foundations and Python implementation. Master learning agents, Q-tables, epsilon-greedy strategies, and code a simple environment from scratch.

• YouTube
• 39 minutes
• Self-Paced
• Free Video