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…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
