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jgrusewski 6192bd4eb6 spec: Q-learning improvements — n-step + IQN ensemble + noisy nets
Three-phase plan to push Q convergence past profitability:
1. N-step returns (n=10): 10× more direct reward signal
2. IQN complementary head alongside C51: ensemble action selection
3. Noisy linear layers: state-dependent exploration

C51 stays for the confidence gate's distributional LCB. IQN adds
flexible quantile estimation. Ensemble combines both for action
selection. All ISV-driven.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-25 20:07:10 +02:00
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