Q-learning Courses and Certifications

Great Learning

Reinforcement Learning in Artificial Intelligence - Full Course

Keeping the importance of Reinforcement Learning in mind, we have come up with this full course by Great Learning Faculty Dr. Narayana Darapaneni. Dr. Narayana holds a PhD in Mathematics from Pierre and Marie Curie University, Paris, France.

• YouTube
• 4 hours 26 minutes
• On-Demand
• Free Video

Great Learning

Machine Learning with Python - Tutorial for Beginners

Comprehensive Python-based machine learning tutorial covering data manipulation, visualization, statistics, and reinforcement learning, with practical examples and a smart taxi case study.

• YouTube
• 10 hours 37 minutes
• On-Demand
• Free Video

The Julia Programming Language

MDPs - Markov Decision Processes - Decision Making Under Uncertainty Using POMDPs.jl

Explore Markov Decision Processes and decision-making under uncertainty using POMDPs.jl. Learn about MDPs, Grid World, solvers, reinforcement learning, and visualization techniques in Julia.

• YouTube
• 49 minutes
• On-Demand
• Free Video

Steve Brunton

Reinforcement Learning - Machine Learning Meets Control Theory

Explore reinforcement learning's intersection with control theory, covering key concepts, algorithms, and applications in this comprehensive overview of machine learning's powerful technique.

• YouTube
• 26 minutes
• On-Demand
• Free Video

Edan Meyer

Q Learning Tutorial - Training Loop

Explore the DQN training loop, covering policy implementation, reward calculation, and key steps in reinforcement learning algorithm implementation.

• YouTube
• 21 minutes
• On-Demand
• Free Video

Edan Meyer

Q Learning Tutorial - Experience Replay

Explore Experience Replay in Q-Learning, focusing on data formatting and implementation. Learn key techniques for improving reinforcement learning algorithms in this hands-on tutorial.

• YouTube
• 27 minutes
• On-Demand
• Free Video

Yannic Kilcher

Fast Reinforcement Learning With Generalized Policy Updates - Paper Explained

Explanation of a novel reinforcement learning framework that enables agents to transfer knowledge between tasks, potentially solving new problems with zero-shot learning through generalized policy updates.

• YouTube
• 55 minutes
• On-Demand
• Free Video

Pascal Poupart

Deep Recurrent Q-Networks

Explore deep reinforcement learning with recurrent neural networks, focusing on LSTM units, Q-learning, and their applications in sequential decision-making problems.

• YouTube
• 28 minutes
• On-Demand
• Free Video

Pascal Poupart

Deep Q-Networks

Learn advanced reinforcement learning techniques, focusing on Deep Q-Networks. Explore approximation, optimization, and practical implementations to enhance AI decision-making capabilities.

• YouTube
• 41 minutes
• On-Demand
• Free Video

Institute for Advanced Study

Provably Efficient Reinforcement Learning with Linear Function Approximation - Chi Jin

Explore efficient reinforcement learning with linear function approximation, covering key concepts like sample efficiency, value-based algorithms, and exploration in multi-armed bandits and linear MDPs.

• YouTube
• 28 minutes
• On-Demand
• Free Video

Simons Institute

Formal Languages and Automata for Reward Function Specification and Efficient Reinforcement Learning

Explore formal languages and automata for specifying reward functions in reinforcement learning, focusing on Linear Temporal Logic and Reward Machines to address real-world RL challenges.

• YouTube
• 1 hour 24 minutes
• On-Demand
• Free Video

Steve Brunton

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

Explore Q-learning and temporal difference algorithms in reinforcement learning, including SARSA and connections to biological learning through dopamine. Covers key concepts for deep reinforcement learning.

• YouTube
• 36 minutes
• On-Demand
• Free Video

Steve Brunton

Model Based Reinforcement Learning - Policy Iteration, Value Iteration, and Dynamic Programming

Explore dynamic programming in model-based reinforcement learning, covering policy iteration, value iteration, and Q-learning for effective decision-making strategies.

• YouTube
• 27 minutes
• On-Demand
• Free Video

Simons Institute

Reinforcement Learning

Explore reinforcement learning's foundations, applications, and advanced concepts, including Q-learning, exploration-exploitation trade-offs, and instance-optimality in policy evaluation.

• YouTube
• 31 minutes
• On-Demand
• Free Video

Jeff Heaton

Introduction to Q-Learning for Game Play - Lecture 12.2

Explore Q-Learning for game AI: build policy tables, suggest optimal actions, and apply Python techniques for environment-based decision-making in this introduction.

• YouTube
• 24 minutes
• On-Demand
• Free Video