Course Overview
Ready to dive into the world of Artificial Intelligence?
This course will guide you through building self-learning agents using Reinforcement Learning (RL) algorithms. Whether you're a beginner or have some experience, this course will provide you with the tools to create agents that can learn from their environment and make decisions.
In this comprehensive course, we cover key concepts and tools, including:
Reinforcement Learning Basics – Understanding agents, environments, and rewards
Markov Decision Processes (MDPs) – Core framework for RL problems
Q-Learning & Deep Q Networks (DQN) – Techniques for training RL agents
Policy Gradient Methods – Advanced strategies for better learning
Model-Free vs Model-Based Learning – Understanding the two main paradigms
Real-World Applications – From robotics to gaming and beyond!
What you will learn
Introduction to Reinforcement Learning (RL) concepts and algorithms.
Implementing Q-Learning for decision-making in dynamic environments.
Using Deep Q-Networks (DQN) to train agents on complex tasks.
Exploring policy gradient methods for continuous action spaces.
Developing multi-agent systems and understanding their interactions.
FINAL PROJECT: Build a self-learning agent for a real-world problem using RL techniques.
Course Curriculum
35
Lessons
(
8 hours
)
Course Review
David
The course on Self-Learning Agents with RL was insightful and engaging. I particularly enjoyed learning how reinforcement learning can help create intelligent agents that learn from their environment. The real-world applications discussed were extremely practical and relevant. It’s a must-take for anyone interested in AI and autonomous systems.
Martin
Self-Learning Agents with RL offers a deep dive into one of the most exciting areas of AI. The course covered essential concepts such as reward functions and training methods effectively. I was impressed by how the course balanced theory with hands-on coding examples. Definitely an eye-opener for anyone passionate about machine learning!
