Value-Based Deep RL Scales Predictably
Oleh Rybkin, Michal Nauman, Preston Fu, Charlie Snell, Pieter Abbeel, Sergey Levine, Aviral Kumar
2025-02-10
Summary
This paper talks about how value-based deep reinforcement learning (RL) methods can scale predictably when given more data or computing power, challenging the belief that these methods behave unpredictably.
What's the problem?
Many people think that scaling value-based RL algorithms, which are used to train AI systems to make decisions, is unpredictable. This makes it hard to plan how much data or compute is needed to achieve certain performance levels, leading to wasted resources or inefficient training.
What's the solution?
The researchers showed that the relationship between data, compute, and performance follows predictable patterns controlled by something called the updates-to-data (UTD) ratio. They created a way to estimate these patterns using small-scale experiments, allowing them to predict resource needs for larger scales. They also optimized hyperparameters like batch size and learning rate to balance data and compute effectively and avoid problems like overfitting.
Why it matters?
This matters because it helps developers plan AI training more efficiently, saving time and resources while achieving better results. Predictable scaling makes it easier to use RL in real-world applications like robotics or gaming, where resources are often limited but high performance is essential.
Abstract
Scaling data and compute is critical to the success of machine learning. However, scaling demands predictability: we want methods to not only perform well with more compute or data, but also have their performance be predictable from small-scale runs, without running the large-scale experiment. In this paper, we show that value-based off-policy RL methods are predictable despite community lore regarding their pathological behavior. First, we show that data and compute requirements to attain a given performance level lie on a Pareto frontier, controlled by the updates-to-data (UTD) ratio. By estimating this frontier, we can predict this data requirement when given more compute, and this compute requirement when given more data. Second, we determine the optimal allocation of a total resource budget across data and compute for a given performance and use it to determine hyperparameters that maximize performance for a given budget. Third, this scaling behavior is enabled by first estimating predictable relationships between hyperparameters, which is used to manage effects of overfitting and plasticity loss unique to RL. We validate our approach using three algorithms: SAC, BRO, and PQL on DeepMind Control, OpenAI gym, and IsaacGym, when extrapolating to higher levels of data, compute, budget, or performance.