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)

Lecture 5, Part 4 (CS 285)

Sergey Levine UC Berkeley

Professor

15:41·2020·graduate level

The lecture concludes with an exploration of actor-critic methods, illustrating how they combine policy-based and value-based approaches to improve learning efficiency.

AI & ML Reinforcement Learning Actor-Critic Methods Policy-Based RL Value-Based RL

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Lecture 5, Part 3 (CS 285)

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Lecture 6, Part 1 (CS 285)

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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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