You're correct that RL has been struggling. Not because of the impressive results by LLMs and image generators, but because the progress within RL has been very slow. People who say otherwise have just forgotten what fast progress looks like; remember 2015-2018 when we first saw human-level Atari play, superhuman Go play, and then superhuman Atari play, as well as impressive results in Starcraft and Dota. I think if you'd asked someone back in 2018 what the next 5 years of RL would look like they would have expected progressively more complicated games to fall, and for agents to graduate from playing with game-state information, as AlphaStar and OpenAI Five did, to besting humans on a level playing field by playing based off of the pixels on the screen the way that agents in Atari do. This hasn't happened.
Instead it turned out that all of this progress was constrained to narrow fields; specifically, games with highly limited input spaces (hence why OpenAI Five and AlphaStar had to take the gamestate directly, which means they get access to information that humans don't) and games where exploration is easy (can be handled in large part or entirely by making random moves some percentage of the time).
I don't think this means the field is dead mind you but it certainly hasn't been making much progress lately.
307thML t1_ja85qu7 wrote
Reply to [D] Is RL dead/worth researching these days? by [deleted]
You're correct that RL has been struggling. Not because of the impressive results by LLMs and image generators, but because the progress within RL has been very slow. People who say otherwise have just forgotten what fast progress looks like; remember 2015-2018 when we first saw human-level Atari play, superhuman Go play, and then superhuman Atari play, as well as impressive results in Starcraft and Dota. I think if you'd asked someone back in 2018 what the next 5 years of RL would look like they would have expected progressively more complicated games to fall, and for agents to graduate from playing with game-state information, as AlphaStar and OpenAI Five did, to besting humans on a level playing field by playing based off of the pixels on the screen the way that agents in Atari do. This hasn't happened.
Instead it turned out that all of this progress was constrained to narrow fields; specifically, games with highly limited input spaces (hence why OpenAI Five and AlphaStar had to take the gamestate directly, which means they get access to information that humans don't) and games where exploration is easy (can be handled in large part or entirely by making random moves some percentage of the time).
I don't think this means the field is dead mind you but it certainly hasn't been making much progress lately.