Uploaded February 2022 | Updated September 2026, 9 hours ago
VISR is a paper about rapid generalization to new tasks in Reinforcement Learning (RL). The full paper was released by DeepMind in 2020 and is called Fast Task Inference with Variational Intrinsic Successor Features. It uses successor features and goal-conditioned policies to rapidly adapt to new tasks after learning within the no-reward regime of RL.
Link to paper: openreview.net/forum?id=BJeAHkrYDS
VISR is a paper about rapid generalization to new tasks in Reinforcement Learning (RL). The full paper was released by DeepMind in 2020 and is called Fast Task Inference with Variational Intrinsic Successor Features. It uses successor features and goal-conditioned policies to rapidly adapt to new tasks after learning within the no-reward regime of RL.
Link to paper: openreview.net/forum?id=BJeAHkrYDS
![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")
