Reinforcement Learning Course - Full Machine Learning Tutorial

Video tutorial on reinforcement learning that combines theory with hands-on coding. Covers the fundamentals of reinforcement learning, Markov decision processes, the explore-exploit dilemma, SARSA and double Q learning in the OpenAI Gym, deep Q learning implemented in both TensorFlow and PyTorch (Q network, agent and main training loop), policy gradient methods applied to Lunar Lander and Space Invaders, and how to build a custom reinforcement learning environment. Suited to programmers already comfortable with Python and neural network basics who want to implement RL agents themselves.

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 .