MIT 6.S191 (2023): Reinforcement Learning @AAmini
MIT 6.S191 (2023): Reinforcement Learning @AAmini
Uploaded April 2023 | Updated September 2026, 2 weeks ago
MIT Introduction to Deep Learning 6.S191: Lecture 5
Deep Reinforcement Learning
Lecturer: Alexander Amini
2023 Edition

For all lectures, slides, and lab materials: introtodeeplearning.com

Lecture Outline:
0:00 - Introduction
3:49 - Classes of learning problems
6:48 - Definitions
12:24 - The Q function
17:06 - Deeper into the Q function
21:32 - Deep Q Networks
29:15 - Atari results and limitations
32:42 - Policy learning algorithms
36:42 - Discrete vs continuous actions
39:48 - Training policy gradients
47:17 - RL in real life
49:55 - VISTA simulator
52:04 - AlphaGo and AlphaZero and MuZero
56:34 - Summary


Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!
MIT 6.S191 (2023): Reinforcement LearningMIT 6.S191 (2021): Convolutional Neural NetworksMIT 6.S191 (2020): Neural RenderingMIT 6.S191 (2021): Deep Generative ModelingGTC 2018: Learning Steering for Parallel Autonomy. Alexander AminiMIT 6.S191 (2025): A Hipocratic Oath, for *your* AI (Comet ML)MIT 6.S191 (2018): Sequence Modeling with Neural NetworksMIT 6.S191 (2024): Deep Generative ModelingMIT Introduction to Deep Learning (2024) | 6.S191MIT 6.S191 (2023): Deep Learning New FrontiersMIT 6.S191 (2018): Faster ML Development with TensorFlowMIT 6.S191 (2025): Recurrent Neural Networks, Transformers, and Attention
Alexander Amini |

MIT 6.S191 (2023): Reinforcement Learning

SHARE TO X SHARE TO REDDIT SHARE TO FACEBOOK WALLPAPER