Uploaded February 2023 | Updated September 2026, 1 hour ago
AdA is a new algorithm out from DeepMind that combines interesting ideas like curriculum learning, meta reinforcement learning (via RL^2), model-based reinforcement learning, attention, and memory models to develop a prototype for a reinforcement learning foundation model. The results look promising, and the future of this area looks bright!
Outline
0:00 - Intro
1:07 - Example Video
2:40 - ClearML
3:48 - How It Works Overview
4:20 - Meta-Learning & RL
8:20 - Attention & Memory
9:55 - Distillation
12:01 - Auto-Curriculum Learning
15:18 - Results
27:14 - Takeaways & Future Work
ClearML - bit.ly/3GtCsj5
Social Media:
YouTube - youtube.com/c/EdanMeyer
Twitter - twitter.com/ejmejm1
Sources:
AdA Paper: arxiv.org/abs/2301.07608
Museli Paper: arxiv.org/abs/2104.06159
Primacy Bias Paper: arxiv.org/abs/2205.07802
AdA is a new algorithm out from DeepMind that combines interesting ideas like curriculum learning, meta reinforcement learning (via RL^2), model-based reinforcement learning, attention, and memory models to develop a prototype for a reinforcement learning foundation model. The results look promising, and the future of this area looks bright!
Outline
0:00 - Intro
1:07 - Example Video
2:40 - ClearML
3:48 - How It Works Overview
4:20 - Meta-Learning & RL
8:20 - Attention & Memory
9:55 - Distillation
12:01 - Auto-Curriculum Learning
15:18 - Results
27:14 - Takeaways & Future Work
ClearML - bit.ly/3GtCsj5
Social Media:
YouTube - youtube.com/c/EdanMeyer
Twitter - twitter.com/ejmejm1
Sources:
AdA Paper: arxiv.org/abs/2301.07608
Museli Paper: arxiv.org/abs/2104.06159
Primacy Bias Paper: arxiv.org/abs/2205.07802
![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")
