feat(PPO_Bot): bot-relative bullets + scan staleness (STATE_DIM=57)

- Bullet state (indices 44-55): enemy-relative → bot-relative frame
  (bot needs threat vectors to itself for dodging, not to enemy)
- New index 56: scan staleness = min(ticksSinceLastScan / 30, 1.0)
  (gives policy a confidence signal for enemy data freshness)
- warm_start.py updated: 44→57 dim expansion, TARGET_DIM variable
- Tests updated for new state layout

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

tools/warm_start.py

@@ -0,0 +1,66 @@
+#!/usr/bin/env python3
+"""Warm-start TARGET_DIM weights from old 44-dim trained weights."""
+import numpy as np
+from pathlib import Path
+
+SRC = Path("tools/training_runner/snapshots/best_post_maint_r38003")
+DST = Path("PPO_Bot/weights/latest")
+OLD_DIM = 44
+TARGET_DIM = 57  # change this to expand to a different input dimension
+
+# Pad w1 [64, OLD_DIM] -> [64, TARGET_DIM] with zeros (weights and Adam moments)
+for prefix in ("actor", "critic"):
+    # Weight
+    old = np.load(SRC / f"{prefix}_w1.npy")
+    assert old.shape == (64, OLD_DIM), f"unexpected shape {old.shape}"
+    new = np.zeros((64, TARGET_DIM), dtype=old.dtype)
+    new[:, :OLD_DIM] = old
+    np.save(DST / f"{prefix}_w1.npy", new)
+    print(f"  {prefix}_w1: {old.shape} -> {new.shape}")
+
+    # Adam moments for w1: pad same way
+    adam_base = f"adam_{'a' if prefix == 'actor' else 'c'}w1"
+    for moment in ("_m", "_v"):
+        old_m = np.load(SRC / f"{adam_base}{moment}.npy")
+        new_m = np.zeros((64, TARGET_DIM), dtype=old_m.dtype)
+        new_m[:, :OLD_DIM] = old_m
+        np.save(DST / f"{adam_base}{moment}.npy", new_m)
+        print(f"  {adam_base}{moment}: {old_m.shape} -> {new_m.shape}")
+
+# Copy unchanged weight files as-is
+unchanged = [
+    "actor_w2", "actor_w3", "actor_b1", "actor_b2", "actor_b3",
+    "critic_w2", "critic_w3", "critic_b1", "critic_b2", "critic_b3",
+    "log_std",
+]
+for name in unchanged:
+    data = np.load(SRC / f"{name}.npy")
+    np.save(DST / f"{name}.npy", data)
+    print(f"  {name}: {data.shape} copied")
+
+# Copy unchanged Adam moments (all except w1, which were handled above)
+unchanged_adam = [
+    "adam_aw2", "adam_cw2",
+    "adam_aw3", "adam_cw3",
+    "adam_ab1", "adam_cb1",
+    "adam_ab2", "adam_cb2",
+    "adam_ab3", "adam_cb3",
+    "adam_logstd",
+]
+for base in unchanged_adam:
+    for moment in ("_m", "_v"):
+        data = np.load(SRC / f"{base}{moment}.npy")
+        np.save(DST / f"{base}{moment}.npy", data)
+        print(f"  {base}{moment}: {data.shape} copied")
+
+(DST / "adam_t.txt").write_text("1\n")
+(DST.parent / "round_counter.txt").write_text("0\n")
+print("  adam_t.txt -> 1, round_counter.txt -> 0")
+
+# Verify
+w1 = np.load(DST / "actor_w1.npy")
+old_w1 = np.load(SRC / "actor_w1.npy")
+assert w1.shape == (64, TARGET_DIM), f"bad shape {w1.shape}"
+assert np.allclose(w1[:, :OLD_DIM], old_w1), "old columns don't match"
+assert np.all(w1[:, OLD_DIM:] == 0), "new columns not zero"
+print(f"\nOK: actor_w1 shape={w1.shape}, cols 0-{OLD_DIM-1} match old, cols {OLD_DIM}-{TARGET_DIM-1} are zero")