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...
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...