jgrusewski
eec5494344
fix(dqn): tighten v_range, replace val_loss proxy, add per-action Q diagnostics
- v_range search bounds [5,30] → [2,10] to match EMA-normalized reward
range [-0.45,+0.32] (max discounted return ≈1.2)
- Replace val_loss proxy (max_Q - reward)^2 with Sharpe-based validation
metric: returns -Sharpe so lower = better, economically meaningful
- Add compute_per_action_q_values(): samples 200 states from replay
buffer, logs per-action Q averages (S100/S50/Flat/L50/L100) at each
epoch — exposes whether agent differentiates exposure actions
- Remove dead max_q_values computation (leftover from old proxy)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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