Today we'll be implementing a Reinforcement Learning algorithm named the Double Deep Q Network algorithm. A lot of other videos will use a library like Stable Baselines, however, today we'll be building this completely from scratch. It'll be used to train the computer to play Super Mario Bros on the NES! This is a tutorial aimed at people that have a base level understanding of ML, but not necessarily reinforcement learning. Also, it's perfect if you're looking for a personal project to add to your resume that can be completed in a weekend.
Additionally, if you don't have the resources to train this locally, I highly recommend checking out Google Colab Notebooks!
This is my first ever RU-vid video and I've been really excited to share this with you guys! If there are any questions or if anyone has any tips/advice, please don't hesitate to comment down below!
00:00 Demo & Intro
03:02 Key Reinforcement Learning Vocabulary
07:47 Epsilon-Greedy Approach
09:32 Replay Buffer
10:20 Action-Value Function Intuition
15:19 The DDQN Algorithm
18:39 DDQN Pseudocode
19:39 Implementation in Code
30:21 The AI Beats the Level!
30:56 Conclusion
SOURCE CODE
github.com/Sourish07/Super-Ma...
PAPERS USED AS REFERENCE
Human-level control through deep reinforcement learning
www.nature.com/articles/natur...
Deep Reinforcement Learning with Double Q-learning
arxiv.org/pdf/1509.06461.pdf
DOCUMENTATION
PyTorch Documentation
pytorch.org/tutorials/interme...
pytorch.org/rl/reference/data...
Gymnasium Documentation
gymnasium.farama.org/index.html
gymnasium.farama.org/api/wrap...
TEXTBOOKS
Learning Deep Learning by Magnus Ekman
www.nvidia.com/en-us/training...
Reinforcement Learning, Second Edition by Sutton, Barto
mitpress.mit.edu/978026203924...
OTHER
CNN Explainer
poloclub.github.io/cnn-explai...
Introducing ChatGPT
openai.com/blog/chatgpt
All content on this channel is produced by and is the intellectual property of Sourish Kundu LLC.
12 май 2024