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Sheep training flock of 10 fix?

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Johnny Fernandes
2026-04-23 20:41:48 +01:00
parent 81dc2aca01
commit a13f5d0ff0
1 changed files with 18 additions and 8 deletions
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training/herding_env.py
+18 -8
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@@ -53,11 +53,12 @@ class HerdingEnv(gym.Env):
# ----------------------------------------------------------------------- # -----------------------------------------------------------------------
# Reward weights (progress-based potential shaping + sparse bonuses) # Reward weights (progress-based potential shaping + sparse bonuses)
# ----------------------------------------------------------------------- # -----------------------------------------------------------------------
W_DRIVE = 2.0 # flock COM moved toward pen (per metre, per step) 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_COLLECT = 1.0 # flock radius shrank (per metre, per step)
W_PEN_BONUS = 5.0 # per sheep penned W_APPROACH = 0.3 # stable position signal: dog close to flock COM
W_COMPLETE = 20.0 # all sheep penned W_PEN_BONUS = 5.0 # per sheep penned
W_STEP_COST = 0.002 # time penalty W_COMPLETE = 20.0 # all sheep penned
W_STEP_COST = 0.002 # time penalty
def __init__(self, n_sheep: int = 1, max_steps: int = 2000, def __init__(self, n_sheep: int = 1, max_steps: int = 2000,
render_mode: str = None): render_mode: str = None):
@@ -288,13 +289,22 @@ class HerdingEnv(gym.Env):
com_dist = float(np.linalg.norm(com - self.PEN_CENTER)) com_dist = float(np.linalg.norm(com - self.PEN_CENTER))
# Progress rewards: positive when flock moves toward pen or compacts # Progress rewards: positive when flock moves toward pen or compacts
drive_progress = (self._prev_com_dist - com_dist) * self.W_DRIVE drive_progress = (self._prev_com_dist - com_dist) * self.W_DRIVE
collect_progress = (self._prev_radius - radius) * self.W_COLLECT collect_progress = (self._prev_radius - radius) * self.W_COLLECT
self._prev_com_dist = com_dist self._prev_com_dist = com_dist
self._prev_radius = radius self._prev_radius = radius
reward = drive_progress + collect_progress # 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
else:
approach = 0.0
reward = drive_progress + collect_progress + approach
reward += newly_penned * self.W_PEN_BONUS reward += newly_penned * self.W_PEN_BONUS
reward -= self.W_STEP_COST reward -= self.W_STEP_COST
if n_penned == self.n_sheep: if n_penned == self.n_sheep: