Explore a groundbreaking DeepMind paper on in-context reinforcement learning through algorithm distillation in this 25-minute video. Dive into the concept of teaching an agent to learn reinforcement learning using behavior cloning over a learning history with a Transformer. Examine the algorithm overview, bandit problems, robustness results, and impressive speedup achievements. Gain insights into the potential future implications of this simple yet powerful idea for the field of artificial intelligence and machine learning.
Overview
Syllabus
- Intro
- Why I like this paper
- ClearML
- Algorithm Overview
- Bandits
- Robustness Results
- Speedup Results
- Other Results
- Conclusion
Taught by
Edan Meyer
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