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

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