UC Berkeley CS 285: Deep Reinforcement Learning →0/11

CS 285: Lecture 1, Introduction. Part 2

CS 285: Lecture 1, Introduction. Part 3

CS 285: Lecture 2, Imitation Learning. Part 1

CS 285: Lecture 2, Imitation Learning. Part 2

CS 285: Lecture 2, Imitation Learning. Part 3

CS 285: Lecture 4, Part 1

Lecture 5, Part 1 (CS 285)

Lecture 5, Part 3 (CS 285)

Lecture 5, Part 4 (CS 285)

Lecture 6, Part 1 (CS 285)

Lecture 6, Part 2 (CS 285)

CS 285: Lecture 1, Introduction. Part 2

Sergey Levine UC Berkeley

Associate Professor of Electrical Engineering and Computer Sciences

17:54·2023·graduate level

This segment of CS 285 introduces foundational concepts in deep reinforcement learning, outlining the problem statement, key components of an RL system, and the interplay between deep neural networks and control.

Systems & CS Reinforcement Learning Deep Learning Policy Optimization

Next · Lecture 2

CS 285: Lecture 1, Introduction. Part 3

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Part of course11 lectures

CS 285: Deep Reinforcement Learning

Prof. Sergey Levine

Imitation learning, policy gradients, actor-critic algorithms, value-function Q-learning, model-based RL, offline reinforcement learning, exploration, and inverse RL.

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