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About Levine Deep RL Lecture
This document summarizes research in deep reinforcement learning via imitation learning. It discusses how imitation learning can be used to train agents from demonstration data but often fails due to distribution mismatch. Methods like Dagger address this by collecting on-policy data. The document also presents research in imitation without humans, such as PLATO which uses trajectory optimization to generate training trajectories. It covers applications in manipulation, locomotion and aerial vehicles. Finally, it discusses challenges and frontiers in the field like developing more sample efficient algorithms and methods for automatically evaluating success.
Detailed Information
| Author: | ['Christos Benetatos'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 70 |
| Language: | English |
| Format: | |
| Price: | FREE |
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