Uploaded January 2022 | Updated September 2026, 3 days ago
Options in Reinforcement Learning are a type of temporal abstraction of actions. The RL options framework takes into account the hierarchical nature of actions we see in real life. They allow AI / RL agents to better explore and achieve better results. This has been demonstrated in RL for robotics and for games. This is a very interesting area of Machine Learning that I highly recommend looking more into!
Original options paper: https://people.cs.umass.edu/~barto/courses/cs687/Sutton-Precup-Singh-AIJ99.pdf
Exploration visualization reference: openreview.net/pdf?id=SkeIyaVtwB
Options in Reinforcement Learning are a type of temporal abstraction of actions. The RL options framework takes into account the hierarchical nature of actions we see in real life. They allow AI / RL agents to better explore and achieve better results. This has been demonstrated in RL for robotics and for games. This is a very interesting area of Machine Learning that I highly recommend looking more into!
Original options paper: https://people.cs.umass.edu/~barto/courses/cs687/Sutton-Precup-Singh-AIJ99.pdf
Exploration visualization reference: openreview.net/pdf?id=SkeIyaVtwB
![Self-Supervised RL - Learning Without Data [Zero to Paper]](https://i.ytimg.com/vi/CDKsa06xU0o/mqdefault.jpg "Self-Supervised RL - Learning Without Data [Zero to Paper]
Inverse Reinforcement Learning with Natural Language Goals (LangGoal IRL) offers a way to do sample-efficient IRL and a way to generalize using self-supervised learning. The paper is novel and is a step forward for general AI and ML algorithms. Though it has its cons, I think it is one of the better papers out there that cover RL, IRL, NLP, and generalization.
Zero to Paper playlist: https://www.youtube.com/playlist?list=PL_49VD9KwQ_ONxENRk11jFEI3_pqAwaug
Inverse Reinforcement Learning video: https://www.youtube.com/watch?v=qo355ALvLRI
Paper covered: https://arxiv.org/pdf/2008.06924.pdf")
