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Meta-RL Definition and Importance
- Meta-RL automates reinforcement learning, improving sample efficiency.
- It addresses slow and inefficient learning by training an RL algorithm for specific problem domains.
MAML vs. RL^2
- MAML meta-learns initializations for faster fine-tuning, like pre-training in deep learning.
- RL^2 uses a black box function approximator, enabling zero-shot learning.
Meta-RL Problem Settings
- Meta-RL problems are categorized along two axes: zero/few-shot vs. many-shot, and single-task vs. multi-task.
- Few-shot focuses on quick adaptation, while many-shot learns long-running algorithms; transfer learning is key in the multi-task setting.