MIT 6.S091: Introduction to Deep Reinforcement Learning

First lecture of MIT course 6.S091: Deep Reinforcement Learning, introducing the fascinating field of Deep RL. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo. INFO: Website: https://deeplearning.mit.edu GitHub: https://github.com/lexfridman/mit-deep-learning Slides: http://bit.ly/2HtcoHV Playlist: http://bit.ly/deep-learning-playlist OUTLINE: 0:00 - Introduction 2:14 - Types of learning 6:35 - Reinforcement learning in humans 8:22 - What can be learned from data? 12:15 - Reinforcement learning framework 14:06 - Challenge for RL in real-world applications 15:40 - Component of an RL agent 17:42 - Example: robot in a room 23:05 - AI safety and unintended consequences 26:21 - Examples of RL systems 29:52 - Takeaways for real-world impact 31:25 - 3 types of RL: model-based, value-based, policy-based 35:28 - Q-learning 38:40 - Deep Q-Networks (DQN) 48:00 - Policy Gradient (PG) 50:36 - Advantage Actor-Critic (A2C & A3C) 52:52 - Deep Deterministic Policy Gradient (DDPG) 54:12 - Policy Optimization (TRPO and PPO) 56:03 - AlphaZero 1:00:50 - Deep RL in real-world applications 1:03:09 - Closing the RL simulation gap 1:04:44 - Next step in Deep RL CONNECT: - If you enjoyed this video, please subscribe to this channel. - Twitter: https://twitter.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/lexfridman - Instagram: https://www.instagram.com/lexfridman