Temporal-Difference Learning Courses and Certifications

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

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

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

Dave Churchill

Introduction to Artificial Intelligence: Temporal Difference Learning - Lecture 17

Explore temporal difference learning, on/off-policy methods, and Q-learning. Gain hands-on experience with reinforcement learning algorithms through practical assignments and code examples.

• YouTube
• 1 hour 19 minutes
• Self-Paced
• Free Video

HuggingFace

Temporal Difference Learning for Model Predictive Control - Research Presentation 3

Presentation on Temporal Difference Learning for Model Predictive Control, exploring innovative approaches in robotics and AI. Insights into recent papers and project developments by researcher Nicklas Hansen.

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

Dave Churchill

Intro to Artificial Intelligence - Temporal Difference Learning - Lecture 19

Explore temporal difference learning and its applications in AI, focusing on practical implementations and problem-solving techniques.

• YouTube
• 1 hour 9 minutes
• Self-Paced
• Free Video

Dave Churchill

Intro to Artificial Intelligence - Temporal Difference Learning - Lecture 18

Explore temporal difference learning and its applications in AI, with practical insights for Assignment 5 implementation.

• YouTube
• 1 hour 14 minutes
• Self-Paced
• Free Video

Dave Churchill

Intro to Artificial Intelligence - Temporal Difference Learning - Lecture 19

Explore temporal difference learning, SARSA, and Q-Learning algorithms with practical examples like the Cliff problem, plus guidance on Assignment 5 implementation for reinforcement learning applications.

• YouTube
• 1 hour 15 minutes
• Self-Paced
• Free Video

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

Decision Making and Reinforcement Learning

25 ratings at Coursera

Model sequential decisions and implement RL algorithms: multi-armed bandits, MDPs and POMDPs, value and policy iteration, Monte Carlo control, SARSA, Q-learning and n-step TD.

• Coursera
• 8 weeks, 6 hours a week
• Self-Paced
• Paid Course

CodeEmporium

Reinforcement Learning 101

Discover the fundamentals of reinforcement learning through multi-armed bandits, Q-learning, deep Q-networks, and policy optimization techniques used in modern AI systems.

• YouTube
• 2 hours 4 minutes
• Self-Paced
• Free Video

Pascal Poupart

Actor Critic

A concise lecture on actor-critic reinforcement learning, from REINFORCE baselines and temporal-difference updates to advantage methods and deterministic policy gradients.

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

Data Science Courses

Deep Learning: Introduction to Reinforcement Learning and Policy Iteration - Lecture 12

Dive into reinforcement learning fundamentals, exploring policy iteration, value iteration, Markov decision processes, and Monte Carlo estimation techniques for advanced AI applications.

• YouTube
• 1 hour 40 minutes
• Self-Paced
• Free Video

Neural Breakdown with AVB

The 6 Questions That Every Reinforcement Learning Algorithm Must Answer

Discover the 6 fundamental questions every RL algorithm must answer, covering Q-learning, policy gradients, actor-critics, and model-based approaches in a clear framework.

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

HuggingFace

Temporal Difference Model Predictive Control (TD-MPC) Explained - Part 1

Dive into the fundamentals of TD-MPC, exploring problem formulation, optimization horizons, and Cross-Entropy Method (CEM) through clear explanations and practical physics examples.

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