Uploaded February 2018 | Updated September 2026, 51 minutes ago
Hey everyone! In this video we started talking about reinforcement learning, my favorite type of deep learning! We take a look at OpenAI's gym, a library used to simulate a game environment for the purpose of testing machine learning agents on games.
In this particular video we built a deep neural network that can learn froma reinforcement learning reward system.
Article that inspired this series: medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-2-ded33892c724
Code to follow along: github.com/ejmejm/CartPole-RL-DNN/blob/master/DeepNNRollout.ipynb
Thank you so much for watching! Please consider leaving a like and subscribing if you found this to be helpful. Also make sure to check out the next video in the series.
Hey everyone! In this video we started talking about reinforcement learning, my favorite type of deep learning! We take a look at OpenAI's gym, a library used to simulate a game environment for the purpose of testing machine learning agents on games.
In this particular video we built a deep neural network that can learn froma reinforcement learning reward system.
Article that inspired this series: medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-2-ded33892c724
Code to follow along: github.com/ejmejm/CartPole-RL-DNN/blob/master/DeepNNRollout.ipynb
Thank you so much for watching! Please consider leaving a like and subscribing if you found this to be helpful. Also make sure to check out the next video in the series.
![Self-Supervised RL - Learning Without Data [Zero to Paper]](https://i.ytimg.com/vi/CDKsa06xU0o/mqdefault.jpg "Self-Supervised RL - Learning Without Data [Zero to Paper]
Inverse Reinforcement Learning with Natural Language Goals (LangGoal IRL) offers a way to do sample-efficient IRL and a way to generalize using self-supervised learning. The paper is novel and is a step forward for general AI and ML algorithms. Though it has its cons, I think it is one of the better papers out there that cover RL, IRL, NLP, and generalization.
Zero to Paper playlist: https://www.youtube.com/playlist?list=PL_49VD9KwQ_ONxENRk11jFEI3_pqAwaug
Inverse Reinforcement Learning video: https://www.youtube.com/watch?v=qo355ALvLRI
Paper covered: https://arxiv.org/pdf/2008.06924.pdf")
