Pixel based RL

Reality에서 Environment 정보는 물리적으로 agent 에게 없으므로 무조건 pixel based 방향성은 맞다

Pixel based representation’s sample efficiency is bad.

Embed trajectory could be very hard since it is heavy

Representation learning for RL

Usually state representation is task-agnostic like

Model based RL

CURL

RAD (Data augmentation tricks enables generating a lot of diversity in limited dataset) == DrQ (concurrent work)

Random crop worked as the best (translation + windowing) and translation is important which results in translation invariance.
RAD improved Pixel SAC better than State SAC.

Is there any other method sample efficiency

SADA (geometric and photometric)

Pixel Data Augmentation for RL

Pixel based RLs

State Representation Sampling

Using learned state representations to sample more effectively from the buffer, potentially prioritizing certain types of experiences that are more beneficial for learning.