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 190.4% mean human performance and 116.0% 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.
This work is supported by the Ministry of Science and Technology of the People’s Republic of China, the 2030 Innovation Megaprojects “Program on New Generation Artificial Intelligence” (Grant No. 2021AAA0150000).
Some have said that poor sample-efficiency on ALE has been a reason to downplay DRL progress or implications. The primary boost in EfficientZero (table 3), pushing it past the human benchmark, is some simple self-supervised learning (SimSiam on predicted vs actual observations).
Holy cow, am I reading that right? RTX3090 costs, like, $2000. So they were able to train this whole thing for about one day's worth of effort using equipment that cost less than $10K in total? That means there's loads of room to scale this up... It means that they could (say) train a version of this architecture with 1000x more parameters and 100x more training data for about $10M and 100 days. Right?
5Razied
You're missing a factor for the number of agents trained (one for each atari game), so in fact this should correspond to about one month of training for the whole game library. More if you want to run each game with multiple random seeds to get good statistics, as you would if you're publishing a paper. But yeah, for a single task like protein folding or some other crucial RL task that only runs once, this could easily be scaled up a lot with GPT-3 scale money.
How well do you think it would generalize? Like, say we made it 1000x bigger and trained it on 100x more training data, but instead of 1 game for 100x longer it was 100 games? Would it be able to do all the games? Would it be better or worse than models specialized to particular games, of similar size and architecture and training data length?
"Mastering Atari Games with Limited Data", Ye et al 2021:
Some have said that poor sample-efficiency on ALE has been a reason to downplay DRL progress or implications. The primary boost in EfficientZero (table 3), pushing it past the human benchmark, is some simple self-supervised learning (SimSiam on predicted vs actual observations).