An Empirical Study of Reinforcement Learning Scaling Laws

Zeyu Zheng, Clare Lyle, Khimya Khetarpal, Will Dabney

The discovery of scaling laws enables prediction of the performance, the optimal resource allocation strategy, and the best hyperparameter configuration of a large-scale training job based on inexpensive experiments at smaller scales. Guided by these empirical scaling laws, the deep learning community has achieved great success in training large (self-)supervised models on Internet-scale datasets. In contrast, despite its potential and wide application, reinforcement learning (RL) has yet to find a guiding principle for predictable scaling. In this work, we conduct an empirical study of RL scaling laws of an actor-critic algorithm in two procedurally generated environments, Starpilot and Craftax. We discover empirical scaling laws with strong predictive performance in both environments. The discovered scaling laws accurately predict the aforementioned three quantities for larger-scale training jobs up to two orders of magnitude in total training compute. In particular, we employ a variant of Elo scores for the single-agent RL paradigm as an alternative performance metric than the commonly used mean episode return. Our results show that the Elo score is a much more predictable…