Capture the experience collector's bottleneck + Q-network forward pass
into a CUDA Graph on first timestep. Prevents ungraphed cublasLtMatmul
calls from contaminating cuBLAS internal state between epochs.
Root cause identified: multiple cuBLAS handles on the same CUDA stream.
NVIDIA docs: "maintain a separate cuBLAS handle for each stream" —
implying one handle per stream. We have 3 handles (experience collector,
training, DDQN) on one stream.
Proper fix (next session): refactor CublasForward to share a single
cuBLAS handle across all components. Separate the handle (shared)
from per-component cached descriptors.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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