Offline Reinforcement Learning

Offline Reinforcement Learning describes training an agent without interacting with the environment. The agent learns from previously collected experiences such as from another RL policy trained online or from a human demonstrator. This video explores two recent advancements in Offline RL!
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Paper Links:
An Optimistic Perspective on Offline Reinforcement Learning: https://arxiv.org/pdf/1907.04543.pdf
Datasets for Data-Driven Reinforcement Learning: https://arxiv.org/pdf/2004.07219.pdf
Q-Learning (Wikipedia): https://en.wikipedia.org/wiki/Q-learning
AVID (Robot in intro animation): https://bair.berkeley.edu/blog/2019/12/13/humans-cyclegan/
Nature-inspired robotics (Robot in intro animation): https://ai.googleblog.com/2020/04/exploring-nature-inspired-robot-agility.html

Видео Offline Reinforcement Learning канала Henry AI Labs