Quick Guide to Reinforcement Learning 🚀

Reinforcement learning is a field of machine learning concerned with how an agent should most optimally take actions in an environment. I think this field of study will be very important in the next few decades, so here I am trying to make the knowledge more accessible.

Patreon: https://www.patreon.com/Gonkee

Resources:
Reinforcement Learning: An Introduction (textbook) - http://incompleteideas.net/book/the-book-2nd.html
OpenAI Spinning Up website - https://spinningup.openai.com/en/latest/index.html
RL subreddit - https://www.reddit.com/r/reinforcementlearning/

Articles:
Deep Reinforcement Learning Doesn't Work Yet - https://www.alexirpan.com/2018/02/14/rl-hard.html
Article about monkey experiments (temporal difference & dopamine) - https://doi.org/10.1126/science.275.5306.1593
2008 article about actor-critic and the striatum - https://doi.org/10.3389/neuro.01.014.2008

Cool algorithms/papers:
MuZero - https://deepmind.google/discover/blog/muzero-mastering-go-chess-shogi-and-atari-without-rules/
DreamerV3 - https://danijar.com/project/dreamerv3/
Robot ball-in-a-cup - https://ieeexplore.ieee.org/abstract/document/5152577
Inverse Q-Learning - https://div99.github.io/IQ-Learn/

00:00 - Introduction
04:27 - Markov Decision Processes
16:37 - Grid Example + Monte Carlo
36:22 - Temporal Difference
50:54 - Deep Q Networks
58:45 - Policy Gradients
1:12:06 - Neuroscience
1:20:24 - Limitations & Future Directions
1:31:57 - Conclusion