Sheep training flock _ improver
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+15
-12
@@ -54,6 +54,8 @@ class HerdingEnv(gym.Env):
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# Reward weights (simple per-sheep progress — no phases, no gating)
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# -----------------------------------------------------------------------
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W_PER_SHEEP = 2.0 # progress: sum of per-sheep distance-to-pen reductions
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W_ALIGN = 0.05 # dog on anti-pen side of COM — directional hint only,
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# kept tiny so sit-still is never profitable vs completion
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W_PEN_BONUS = 10.0 # per sheep penned
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W_COMPLETE = 100.0 # all sheep penned
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W_STEP_COST = 0.02 # time penalty — strong enough to punish doing nothing
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@@ -312,7 +314,19 @@ class HerdingEnv(gym.Env):
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else:
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r_progress = 0.0
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reward = r_progress
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com, _, _ = self._flock_stats()
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com_dist = float(np.linalg.norm(com - self.PEN_CENTER))
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d_dog_com = float(np.linalg.norm(self.dog_pos - com))
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if d_dog_com > 0.1 and com_dist > 0.1:
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pen_dir = (self.PEN_CENTER - com) / com_dist
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dog_dir = (self.dog_pos - com) / d_dog_com
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cosine = -float(np.dot(pen_dir, dog_dir))
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proximity = max(0.0, 1.0 - d_dog_com / self.FLEE_DIST)
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alignment = cosine * proximity * self.W_ALIGN
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else:
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alignment = 0.0
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reward = r_progress + alignment
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reward += newly_penned * self.W_PEN_BONUS
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reward -= self.W_STEP_COST
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if n_penned == self.n_sheep:
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@@ -363,17 +377,6 @@ class HerdingEnv(gym.Env):
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if pos[1] > F - m: fy -= ((pos[1] - (F - m)) / m) * 6.0
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# Pen exterior wall avoidance: mirrors sheep.py.
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# Only fires when strictly outside the pen at pen height.
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EM = 0.8
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px0, px1 = self.PEN_X[0], self.PEN_X[1]
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py0, py1 = self.PEN_Y[0], self.PEN_Y[1]
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if py0 < pos[1] < py1:
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if px0 - EM < pos[0] < px0:
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fx -= ((pos[0] - (px0 - EM)) / EM) * 6.0
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if px1 < pos[0] < px1 + EM:
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fx += ((px1 + EM - pos[0]) / EM) * 6.0
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# Hard-stop clamp: mirrors sheep.py — zero any force driving further
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# into the wall within 0.5 m so the flee force cannot pin the sheep.
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HS = 0.5
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