diff --git a/training/herding_env.py b/training/herding_env.py
index 52a0e7b..4488fa2 100644
--- a/training/herding_env.py
+++ b/training/herding_env.py
@@ -10,10 +10,10 @@ Coordinate system matches the Webots world file:
     field  : x ∈ [-15, 15],  y ∈ [-15, 15]
     pen    : x ∈ [10, 13],   y ∈ [-15, -8]   (SE corner, open north)
 
-Observation (13-dim, fixed regardless of n_sheep):
-    dog position (2), flock COM relative to dog (2), farthest active sheep
-    relative to dog (2), pen relative to COM (2), pen relative to farthest
-    sheep (2), flock radius (1), mean dispersion (1), fraction penned (1).
+Observation (16-dim, fixed regardless of n_sheep):
+    dog position (2), flock COM relative to dog (2), top-3 farthest active
+    sheep relative to dog (6), pen relative to COM (2), pen relative to
+    farthest sheep (2), flock radius (1), fraction penned (1).
 
 Permutation-invariant by design: curriculum stages share the same obs dim
 so VecNormalize statistics transfer as n_sheep advances.
@@ -72,11 +72,11 @@ class HerdingEnv(gym.Env):
         self.render_mode    = render_mode
         self.random_n_sheep = random_n_sheep   # if True, randomise n_sheep each reset
 
-        # Fixed 17-dim observation regardless of n_sheep:
+        # Fixed 16-dim observation regardless of n_sheep:
         #   dog_pos(2) + rel_com(2) + rel_far1(2) + rel_far2(2) + rel_far3(2)
         #   + com_to_pen(2) + far1_to_pen(2) + radius(1) + frac_penned(1)
         self.observation_space = spaces.Box(
-            low=-np.inf, high=np.inf, shape=(17,), dtype=np.float32
+            low=-np.inf, high=np.inf, shape=(16,), dtype=np.float32
         )
 
         # Action: desired velocity (vx, vy) ∈ [-1, 1]², scaled by DOG_SPEED