3. Machine Learning vs. Human
● Machine Learning
○ Try to learn data points
■ Supervised/Unsupervised Learning
■ Reinforcement Learning
● Human
○ Fast adaptation with prior knowledge
■ Few-shot learning
■ Generalization across tasks
3
4. ● Related Work
○ LEARNING TO REINFORCEMENT LEARN
○ RL^2
○ MAML
○ Auto-Meta
Meta-Learner?
Multi-armed bandit problem
https://blog.floydhub.com/meta-rl/
4
5. Meta-RL
● Goal: Generalization across tasks
● Notations
○ T: Task distribution e.g., driving, multi-armed bandit problems
○ T_i: Specific task e.g., Sonata, Porsche, ...
○ x_t: state
○ a_t: action
5
9. Attention is All you Need(2017)
https://mchromiak.github.io/articles/2017/Sep/12/Transformer-Attention-is-all-you-need/#.XJ6U6-szZ0c
https://medium.com/@hyponymous/paper-summary-attention-is-all-you-need-22c2c7a5e06
Q: Hidden State of Decoder
K: Hidden State of Encoder
V: (normalized) Weights
9
PR-049: https://www.youtube.com/watch?v=6zGgVIlStXs
10. Motivation
● Temporal(Causal) Convolution
○ depends on previous steps
● Soft Attention
○ weighted sum
https://www.slideshare.net/ThomasHjeldeThoresen/temporal-convolutional-networks-dethroning-rnns-for-sequence-modelling
https://medium.com/syncedreview/memory-attention-sequences-8522f531dd43
10