v0.6.0 — Bid V5 IS-DD (score-aware champion)
Default bid model → Bid V5 IS-DD
The default bidder is now Bid V5 IS-DD (25M training steps, score-aware v2 observations, IS-DD-only reward pool).
Key improvements vs v3 Max
| Metric | v3 Max | v5 IS-DD |
|---|---|---|
| DMC play arena vs v2 champion | −1.5% | +11.0% |
| IS-DD play arena | +5.9% | +14.6% |
| Opening XGB-baseline accuracy (h0 probe) | — | 99% |
What's new
- Score-aware features (5 extra dims: my/opp normalized, win_prob, leader_dist, diff) — obs_dim = 113
- Reward clipping + Polyak EMA (τ = 0.005) + cosine LR decay for training stability
- IS-DD-only reward pool (instead of
max(DMC, IS-DD)) — makes the bidder a true IS-DD synergy multiplier
Breaking change
obs_dim changed from 108 → 113. The colver-py bridge now dispatches on net.obs_dim() to build the right observation; old 108-dim models (v2, v3, v4) still work via the same entry point. Web frontend auto-loads v5 by default (match-neutral score context for single-deal play).
Assets
bid_v5_isdd.bin— Bid V5 IS-DD weights (2.3 MB, 512 hidden × 3 layers, dueling)
Companion docs
- Human-usable strategy guide (88-94% agreement with NN)
- Hidden-layer probe analysis — 2 features discovered that close the XGBoost gap from 77% to 97% on defense
/annoncerweb page — visual strategy reference