DQN was published in 2013, EfficientZero in 2021. That's 8 years with 500 times improvement.
So data efficiency was doubling roughly every year for the past 8 years.
Side note: EfficientZero I think still may not be super-human on games like Montezuma's Revenge.
https://arxiv.org/abs/2111.00210
Reinforcement learning has achieved great success in many applications. However, sample efficiency remains a key challenge, with prominent methods requiring millions (or even billions) of environment steps to train. Recently, there has been significant progress in sample efficient image-based RL algorithms; however, consistent human-level performance on the Atari game benchmark remains an elusive goal. We propose a sample efficient model-based visual RL algorithm built on MuZero, which we name EfficientZero. Our method achieves 194.3% mean human performance and 109.0% median performance on the Atari 100k benchmark with only two hours of real-time game experience and outperforms the state SAC in some tasks on the DMControl 100k benchmark. This is the first time an algorithm achieves super-human performance on Atari games with such little data. EfficientZero's performance is also close to DQN's performance at 200 million frames while we consume 500 times less data. EfficientZero's low sample complexity and high performance can bring RL closer to real-world applicability. We implement our algorithm in an easy-to-understand manner and it is available at this https URL. We hope it will accelerate the research of MCTS-based RL algorithms in the wider community.
It may not be immediately clear, but it is nevertheless unfortunately clear from RL papers which provide adequate sample-size or compute ranges that RL appears to follow scaling laws (just like everywhere else anyone bothers to test). Yeah, they just get better the same way that regular ol' self-supervised or supervised Transformers do. Sorry if you were counting on 'RL doesn't work' for safety or anything.
If you don't believe the basic existence proofs of things like OA5 or AlphaStar, which work only because things like larger batch sizes or more diverse agent populations magically make notoriously-unreliable archs work, you can look at Jones's beautiful AlphaZero scaling laws (plural) work https://arxiv.org/abs/2104.03113 , or browse through relevant papers https://www.reddit.com/r/mlscaling/search?q=flair%3ARL&restr... https://www.gwern.net/notes/Scaling#ziegler-et-al-2019-paper Or GPT-f. Then you have stuff like Gato continuing to show scaling even in the Decision Transformer framework. Or consider instances of plugging pretrained models into RL agents, like SayCan-PaLM most recently.
The question is if it hits that wall before or after human-level (and/or dangerous) capabilities.
https://ieeexplore.ieee.org/document/9351818
B. R. Kiran et al., "Deep Reinforcement Learning for Autonomous Driving: A Survey," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 6, pp. 4909-4926, June 2022, doi: 10.1109/TITS.2021.3054625.
I haven't read the paper, so this is not a reading recommendation. Just posting as evidence that there is work in the area.
People have thought Deep RL would lead to AGI since practically the beginning of the deep learning revolution, and likely significantly before. It's the most intuitive approach by a longshot (even depicted in movies as agents receiving positive/negative reinforcement from their environment), but that doesn't mean it's the best. RL still faces huge struggles with compute efficiency and it isn't immediately clear that current RL algorithms will neatly scale with data & parameter count.