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 3 (CS 285)

Sergey Levine UC Berkeley

Professor

14:52·2020·graduate level

This part delves into the practical challenges of applying policy gradients, discussing variance reduction techniques such as baseline subtraction and the use of value functions.

AI & ML Reinforcement Learning Policy Gradient Variance Reduction Baseline Subtraction

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Lecture 5, Part 1 (CS 285)

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Lecture 5, Part 4 (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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