Add RMSNorm (root mean square normalization) after each hidden layer
in the distributional-dueling Q-network: shared backbone layers, value
stream, and advantage stream. RMSNorm stabilizes activations and
gradients without the overhead of full LayerNorm (no mean centering),
making the network less sensitive to input scale during training.
Architecture per layer: Linear -> LeakyReLU -> RMSNorm
RMSNorm weights are automatically tracked in the existing VarMap since
they are created via VarBuilder::from_varmap with the same shared map.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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