MIT 6.S191: Reinforcement Learning
MIT Introduction to Deep Learning 6.S191: Lecture 5
Deep Reinforcement Learning
Lecturer: Alexander Amini
2024 Edition
For all lectures, slides, and lab materials: http://introtodeeplearning.com
Lecture Outline:
0:00 - Introduction
2:20 - Classes of learning problems
6:33 - Definitions
12:30 - The Q function
17:29 - Deeper into the Q function
23:12 - Deep Q Networks
30:36 - Atari results and limitations
34:24 - Policy learning algorithms
39:31 - Discrete vs continuous actions
43:21 - Training policy gradients
49:10 - RL in real life
51:33 - VISTA simulator
53:24 - AlphaGo and AlphaZero and MuZero
58:58 - Summary
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MIT Introduction to Deep Learning 6.S191: Lecture 5
Deep Reinforcement Learning
Lecturer: Alexander Amini
2024 Edition
For all lectures, slides, and lab materials: http://introtodeeplearning.com
Lecture Outline:
0:00 – Introduction
2:20 – Classes of learning problems
6:33 – Definitions
12:30 – The Q function
17:29 – Deeper into the Q function
23:12 – Deep Q Networks
30:36 – Atari results and limitations
34:24 – Policy learning algorithms
39:31 – Discrete vs continuous actions
43:21 – Training policy gradients
49:10 – RL in real life
51:33 – VISTA simulator
53:24 – AlphaGo and AlphaZero and MuZero
58:58 – 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!!
