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Sheep training flock of 10 fix?

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Johnny Fernandes
2026-04-23 23:20:23 +01:00
parent a13f5d0ff0
commit f68dea44da
1 changed files with 16 additions and 12 deletions
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training/herding_env.py
+16 -12
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@@ -53,9 +53,9 @@ class HerdingEnv(gym.Env):
# -----------------------------------------------------------------------
# Reward weights (progress-based potential shaping + sparse bonuses)
# -----------------------------------------------------------------------
W_DRIVE = 2.0 # flock COM moved toward pen (per metre, per step)
W_COLLECT = 1.0 # flock radius shrank (per metre, per step)
W_APPROACH = 0.3 # stable position signal: dog close to flock COM
W_DRIVE = 2.0 # progress: flock COM moved toward pen
W_COLLECT = 0.5 # progress: flock radius shrank
W_ALIGN = 0.5 # position: dog on anti-pen side of flock COM
W_PEN_BONUS = 5.0 # per sheep penned
W_COMPLETE = 20.0 # all sheep penned
W_STEP_COST = 0.002 # time penalty
@@ -288,23 +288,27 @@ class HerdingEnv(gym.Env):
com, radius, _ = self._flock_stats()
com_dist = float(np.linalg.norm(com - self.PEN_CENTER))
# Progress rewards: positive when flock moves toward pen or compacts
# Progress rewards: positive when state improves
drive_progress = (self._prev_com_dist - com_dist) * self.W_DRIVE
collect_progress = (self._prev_radius - radius) * self.W_COLLECT
self._prev_com_dist = com_dist
self._prev_radius = radius
# Approach: stable position signal so the dog has a gradient toward
# the flock even when the sheep are not actively fleeing
active_mask = ~self.penned[:self.n_sheep]
if active_mask.any():
dog_to_com = float(np.linalg.norm(self.dog_pos - com))
approach = -(dog_to_com / (2 * self.FIELD)) * self.W_APPROACH
# Alignment: reward dog for being on the anti-pen side of the flock
# COM, gated by proximity so only nearby positioning counts.
# +1 = dog directly behind flock, -1 = dog on pen side (wrong).
d_dog_com = float(np.linalg.norm(self.dog_pos - com))
if d_dog_com > 0.1 and com_dist > 0.1:
pen_dir = (self.PEN_CENTER - com) / com_dist # COM → pen
dog_dir = (self.dog_pos - com) / d_dog_com # COM → dog
cosine = -float(np.dot(pen_dir, dog_dir)) # +1 when opposite
proximity = max(0.0, 1.0 - d_dog_com / self.FLEE_DIST)
alignment = cosine * proximity * self.W_ALIGN
else:
approach = 0.0
alignment = 0.0
reward = drive_progress + collect_progress + approach
reward = drive_progress + collect_progress + alignment
reward += newly_penned * self.W_PEN_BONUS
reward -= self.W_STEP_COST
if n_penned == self.n_sheep: