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On BatchNorm Forward Modes in Value-Based Reinforcement Learning
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 p
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- arXiv · AI, language, vision and robotics · 2026-09-06T06:45:59.000Z
First collected: 2026-09-20T21:12:06.801Z. This is not the publication date.