Per-layer drop with 20% probability during training. Each hidden layer
(h_s1, h_s2, h_v) is independently scaled by 0 (dropped) or 1/(1-p)
(kept, expected-value correction). Only applied to online forward pass
on current states — target and Double-DQN passes use full network.
Implementation:
- New stochastic_depth_scale kernel in dqn_utility_kernels.cu
- Per-layer scale buffer [3] f32 — written by host before each graph
replay (CUDA Graphs capture addresses, not contents)
- Scale kernel inserted in launch_cublas_forward after online Pass 1
- update_stochastic_depth_mask() generates random 0/keep scales per step
- At inference (experience collection), all layers active (no drop)
Forces every layer to produce useful features independently — prevents
deep compositional memorization where removal of any single layer
would collapse the output. First RL trading application of stochastic
depth (proven in Vision Transformers, novel in DQN).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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