CompleteP for RL: Maintaining Feature Learning When Scaling Deep Reinforcement Learning

The maximal update parameterization ($\mu P$) provides an approach to scale parameters while preserving feature learning in deep neural networks. In fixed data distribution settings it has been found to yield more stable learning dynamics, more consistent learned features, and enable optimal hyperparameters such as learning rate to transfer from small models to larger ones. However, it is unclear if these benefits readily transfer to reinforcement learning problems, where learning dynamics are coupled to the non-stationary data distribution induced by an agent's own actions. We empirically study how two regimes, the ''rich'' CompleteP and ''lazy'' Neural Tangent Kernel (NTK) parameterizations affect hyperparameter transfer, feature and policy consistency, and learning behavior as we scale reinforcement learning agents. Ultimately, we show that agents trained using CompleteP consequentially improves compute and reward efficiency compared to the NTK parameterization across multiple control tasks and variants.