Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 6 - Reinforcement Learning Primer
Автор: Stanford Online
Загружено: 2020-02-25
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For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai
Assistant Professor Chelsea Finn, Stanford University
http://cs330.stanford.edu/
0:00 Introduction
0:46 Logistics
2:31 Why Reinforcement Learning?
3:37 The Plan
6:16 Terminology & notation
8:36 Imitation Learning
10:01 Reward functions
10:57 The goal of reinforcement learning
19:15 What is a reinforcement learning task?
21:01 The goal of multi-task reinforcement learning
23:31 The anatomy of a reinforcement learning algorithm
25:48 Evaluating the objective
26:43 Direct policy differentiation
32:02 Evaluating the policy gradient
33:16 Comparison to maximum likelihood
35:54 Example: MAML + policy gradient
37:25 Example: Black-box meta-learning + policy gradient
45:26 Policy Gradients
49:16 Value-Based RL: Definitions
52:14 Fitted Q-iteration Algorithm
56:13 Multi-Task RL Algorithms
58:00 An example
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