MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL)

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

Видео MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL) канала Lex Fridman