On BatchNorm Forward Modes in Value-Based Reinforcement Learning

Abstract:Batch normalization (BN) substantially improves sample efficiency in continuous-control actor-critic methods such as CrossQ, yet recent studies report performance degradation in discrete-action value learning on Atari. These failures are surprising because discrete Q-networks lack the action-input distribution mismatch identified by CrossQ. We show for target-based C51 and target-free PQN that the simple choice between running and batch statistics at specific forward passes can reverse this degradation. In C51, switching the BN bootstrap forward to batch-statistic mode significantly improves performance over unnormalized and LayerNorm baselines and scales stably with update-to-data ratios up to 12. In PQN, using batch-statistics for both action selection and bootstrapping recovers performance from the failing running-statistic configuration. Across 26 Atari games at 400M frames, this configuration achieves a higher final aggregate score than PQN with LayerNorm. Our results show that carefully configured BN can substantially improve discrete-action value learning, and that its forward protocols are an essential part of the algorithm specification.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.06421 [cs.LG]

(or arXiv:2609.06421v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.06421 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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