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MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL) Analytics Table
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About MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL)
Explore MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL) with 373,993 views, 6,814 likes, and 151 comments. Experience the impact of this video content that has captured audience attention.
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
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