Tea_Pearce
Tea_Pearce t1_ja73tpy wrote
Reply to comment by CellWithoutCulture in [D] Is RL dead/worth researching these days? by [deleted]
fyi, GATO used imitation learning, which is closer to supervised than RL.
Tea_Pearce OP t1_j4689f6 wrote
Reply to comment by JustOneAvailableName in [D] Bitter lesson 2.0? by Tea_Pearce
fair point, I suppose that timeframe was simply used to be consistent with the original lesson.
Tea_Pearce t1_j0tvm4v wrote
This repo is a single minimal script that does conditional generation on MNIST: https://github.com/TeaPearce/Conditional_Diffusion_MNIST
Tea_Pearce t1_iwfyifj wrote
this is gold! great write up 👍
Tea_Pearce t1_ja753ng wrote
Reply to [D] Is RL dead/worth researching these days? by [deleted]
Imo it depends on what you mean by RL. If you interperet RL as the 2015-19 collection of algorithms that train deep NN agents tabula rasa (from zero knowledge), I'd be inclined to agree that it doesn't seem a particularly fruitful research direction to get into. But if you interperet RL as a general problem setting, where an agent must learn in a sequential decision making environment, you'll see that it's not going away.
To me the most interesting recent research in RL (or whatever you want to name it) is figuring out how to leverage existing datasets or models to get agents working well in sequential environments. Think SayCan, ChatGPT, Diffusion BC...