Stanford CS330: Multi-Task and Meta-Learning, 2019 | Lecture 7 - Kate Rakelly (UC Berkeley)
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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
Kate Rakelly (UC Berkeley) Guest Lecture in Stanford CS330
http://cs330.stanford.edu/
0:00 Introduction
0:17 Lecture outline
1:07 Recap: meta-reinforcement learning
3:55 What's different in RL?
5:33 PG meta-RL algorithms: recurrent Implement the policy as a recurrent network, train
7:41 PG meta-RL algorithms: gradients
9:57 How these algorithms learn to explore
15:27 What's the problem?
22:45 Meta-RL desiderata
28:43 Model belief over latent task variables POMDP for unobserved state
33:49 Posterior sampling in action
35:07 Meta-RL with task-belief states
38:18 Encoder design
43:45 Integrating task-belief with SAC
46:23 Separate task-Inference and RL data
52:16 Limits of posterior sampling
55:06 Summary
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