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Sheep training flock _ improver

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Johnny Fernandes
2026-04-24 21:29:44 +01:00
parent 8b54b2a934
commit d599181d22
2 changed files with 37 additions and 5 deletions
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training/herding_env.py
+24 -5
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@@ -53,10 +53,12 @@ class HerdingEnv(gym.Env):
# -----------------------------------------------------------------------
# Reward weights (simple per-sheep progress — no phases, no gating)
# -----------------------------------------------------------------------
W_PER_SHEEP = 2.0 # progress: sum of per-sheep distance-to-pen reductions
W_PEN_BONUS = 10.0 # per sheep penned
W_COMPLETE = 100.0 # all sheep penned
W_STEP_COST = 0.02 # time penalty — strong enough to punish doing nothing
W_PER_SHEEP = 2.0 # progress: sum of per-sheep distance-to-pen reductions
W_SCATTER_PEN = 0.5 # penalty per metre the active flock radius exceeds threshold
SCATTER_THRESH = 8.0 # metres — allow natural spread, penalise excessive scatter
W_PEN_BONUS = 10.0 # per sheep penned
W_COMPLETE = 100.0 # all sheep penned
W_STEP_COST = 0.02 # time penalty — strong enough to punish doing nothing
def __init__(self, n_sheep: int = 1, max_steps: int = 2000,
render_mode: str = None, random_n_sheep: bool = False):
@@ -312,7 +314,12 @@ class HerdingEnv(gym.Env):
else:
r_progress = 0.0
reward = r_progress
# Soft scatter penalty: discourages abandoning the remaining active flock.
# Only fires when radius exceeds threshold so normal spread isn't punished.
_, radius, _ = self._flock_stats()
r_scatter = -max(0.0, radius - self.SCATTER_THRESH) * self.W_SCATTER_PEN
reward = r_progress + r_scatter
reward += newly_penned * self.W_PEN_BONUS
reward -= self.W_STEP_COST
if n_penned == self.n_sheep:
@@ -362,6 +369,18 @@ class HerdingEnv(gym.Env):
if pos[1] < -F + m: fy += ((-F + m - pos[1]) / m) * 6.0
if pos[1] > F - m: fy -= ((pos[1] - (F - m)) / m) * 6.0
# Pen exterior wall avoidance — mirrors sheep.py addition.
# Prevents sheep getting pinned against the pen side/back walls when fleeing.
EM = 1.2
px0, px1 = self.PEN_X[0], self.PEN_X[1]
py0, py1 = self.PEN_Y[0], self.PEN_Y[1]
if py0 - EM < pos[1] < py1 and pos[0] < px0 + EM:
fx -= ((px0 + EM - pos[0]) / EM) * 8.0
if py0 - EM < pos[1] < py1 and pos[0] > px1 - EM:
fx += ((pos[0] - (px1 - EM)) / EM) * 8.0
if pos[1] < py0 + EM and px0 < pos[0] < px1:
fy += ((py0 + EM - pos[1]) / EM) * 8.0
# Hard-stop clamp: mirrors sheep.py — zero any force driving further
# into the wall within 0.5 m so the flee force cannot pin the sheep.
HS = 0.5