CS 285: Lecture 1, Introduction. Part 2
17:54CS 285: Lecture 1, Introduction. Part 3
29:01CS 285: Lecture 2, Imitation Learning. Part 1
24:47CS 285: Lecture 2, Imitation Learning. Part 2
23:06CS 285: Lecture 2, Imitation Learning. Part 3
32:23CS 285: Lecture 4, Part 1
26:28Lecture 5, Part 1 (CS 285)
14:02Lecture 5, Part 3 (CS 285)
14:52Lecture 5, Part 4 (CS 285)
15:41Lecture 6, Part 1 (CS 285)
25:13Lecture 6, Part 2 (CS 285)
17:29CS 285: Lecture 4, Part 1
Associate Professor of Electrical Engineering and Computer Sciences
This lecture segment from UC Berkeley's CS 285 introduces foundational concepts in deep reinforcement learning, detailing policy gradient methods and their application in training agents with neural networks. It covers the mathematical formulation of reinforcement learning problems and the challenges of high-dimensional state spaces.
CS 285: Lecture 2, Imitation Learning. Part 3
Next · Lecture 7Lecture 5, Part 1 (CS 285)
Seminar notes & discussion
No public seminar notes on this lecture yet.