Dive into a comprehensive tutorial on reinforcement learning, covering core topics from Q learning and SARSA to advanced techniques like deep Q learning and policy gradient methods. Explore practical implementations using TensorFlow and PyTorch in various OpenAI Gym environments, including Space Invaders and Breakout. Begin with modern algorithms to demonstrate the power of reinforcement learning, then delve into fundamental concepts underpinning all reinforcement learning algorithms. Learn to code your own reinforcement learning environment, understand Markov Decision Processes, and tackle the explore-exploit dilemma. Gain hands-on experience with SARSA and Double Q Learning in the OpenAI Gym, providing a solid foundation for mastering this exciting field of machine learning.
Overview
Syllabus
Intro .
Intro to Deep Q Learning .
How to Code Deep Q Learning in Tensorflow .
Deep Q Learning with Pytorch Part 1: The Q Network .
Deep Q Learning with Pytorch part 2: Coding the Agent .
Deep Q Learning with Pytorch part.
Intro to Policy Gradients 3: Coding the main loop .
How to Beat Lunar Lander with Policy Gradients .
How to Beat Space Invaders with Policy Gradients .
How to Create Your Own Reinforcement Learning Environment Part 1 .
How to Create Your Own Reinforcement Learning Environment Part 2 .
Fundamentals of Reinforcement Learning .
Markov Decision Processes .
The Explore Exploit Dilemma .
Reinforcement Learning in the Open AI Gym: SARSA .
Reinforcement Learning in the Open AI Gym: Double Q Learning .
Conclusion .
Taught by
freeCodeCamp.org
Related Courses
-
Understanding Algorithms for Reinforcement Learning
-
Deep Reinforcement Learning in Python
-
Modern Reinforcement Learning: Deep Q Learning in PyTorch
-
Python Reinforcement Learning using OpenAI Gymnasium – Full Course
-
Reinforcement Learning with Gymnasium in Python
-
Deep Reinforcement Learning: Hands-on AI Tutorial in Python
