Full design for monolithic CUDA kernel that runs the entire DQN
experience collection pipeline on GPU: Dueling Q-network inference
(online + target), epsilon-greedy action selection, portfolio
simulation, barrier tracking, diversity entropy, curiosity inference,
and reward combination. 128 parallel episodes × 500 timesteps =
64,000 experiences per kernel launch with zero CPU roundtrips.
Key decisions:
- Approach A (monolithic kernel) for speed + accuracy
- Parallel episode architecture (N threads × L timesteps)
- Q-network + curiosity inference in raw CUDA (matrix-vector multiply)
- Curiosity training stays on Candle (periodic batch updates)
- TD error pre-computed for priority replay
- Weight sync after gradient updates (~1.2MB upload)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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