Johnny Fernandes
544 lines
24 KiB
Python
544 lines
24 KiB
Python
"""
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2D herding environment for PPO training (Gymnasium-compatible).
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The dog agent (action: 2D velocity vector) must herd n_sheep into the
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quarantine pen. Sheep dynamics mirror the Webots controller exactly:
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flee (quadratic ramp), separation (inverse-distance), cohesion, wall
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avoidance, and wander.
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Coordinate system matches the Webots world file:
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field : x ∈ [-15, 15], y ∈ [-15, 15]
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pen : x ∈ [10, 13], y ∈ [-15, -8] (SE corner, open north)
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Observation (16-dim, fixed regardless of n_sheep):
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dog position (2), flock COM relative to dog (2), top-3 farthest active
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sheep relative to dog (6), pen relative to COM (2), pen relative to
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farthest sheep (2), flock radius (1), fraction penned (1).
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Permutation-invariant by design: curriculum stages share the same obs dim
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so VecNormalize statistics transfer as n_sheep advances.
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"""
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import numpy as np
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import gymnasium as gym
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from gymnasium import spaces
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class HerdingEnv(gym.Env):
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metadata = {"render_modes": ["human", "rgb_array"], "render_fps": 30}
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# -----------------------------------------------------------------------
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# World constants — must match Webots world file
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# -----------------------------------------------------------------------
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MAX_SHEEP = 10
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FIELD = 15.0 # half-size; positions ∈ [-FIELD, FIELD]
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PEN_X = (10.0, 13.0)
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PEN_Y = (-15.0, -8.0)
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PEN_CENTER = np.array([11.5, -11.5], dtype=np.float32)
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PEN_ENTRY = np.array([11.5, -8.0], dtype=np.float32) # north entrance face center
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# -----------------------------------------------------------------------
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# Dynamics — calibrated to match Webots robot specs
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# -----------------------------------------------------------------------
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DOG_SPEED = 2.5 # m/s
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SHEEP_FLEE_V = 0.65 # m/s
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SHEEP_WANDER_V = 0.20 # m/s
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DT = 0.1 # seconds per step
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# Boid parameters — identical to sheep.py
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FLEE_DIST = 7.0
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SEPARATION_DIST = 2.5
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COHESION_DIST = 8.0
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WALL_MARGIN = 3.5
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# -----------------------------------------------------------------------
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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 # gated on action magnitude — dog only earns it when moving.
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# Without gating this created a sit-still trap from n_sheep≥2.
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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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W_COMPACT = 0.0 # reward for flock-radius reduction (off by default)
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W_WALL_TOUCH = 0.04 # per-sheep max penalty at wall surface. Linear ramp
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# within WALL_TOUCH_BUFFER nudges the agent to avoid
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# pinning sheep against pen walls. 0.04 ≈ 2× step_cost
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# — noticeable but never dominates progress reward.
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WALL_TOUCH_BUFFER = 0.3 # metres from wall where penalty starts ramping
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ALIGN_SHAPE = "standoff" # "standoff" (peaks at IDEAL) | "near" (peaks at 0)
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ALIGN_GATED = True # gate alignment on action magnitude
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ENTRY_AWARE = False # When True, targets PEN_ENTRY (entrance face) instead
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# of PEN_CENTER for progress/obs. Intended to fix wall-
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# corralling but collapsed n_sheep≥2 success rate.
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# The wall-touch gradient penalty handles wall avoidance
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# without breaking the core herding signal.
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# Initial sheep spawn: first sheep placed anywhere; rest within CLUSTER_RADIUS
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# of it. Set to None for legacy uniform-scatter behaviour.
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# Cluster radius ≤ COHESION_DIST (8m) so boid cohesion keeps the flock together.
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INIT_CLUSTER_RADIUS = 5.0
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def __init__(self, n_sheep: int = 1, max_steps: int = 2000,
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render_mode: str = None, random_n_sheep: bool = False,
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reward_cfg: dict = None):
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super().__init__()
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assert 1 <= n_sheep <= self.MAX_SHEEP
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self.n_sheep = n_sheep
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self.max_steps = max_steps
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self.render_mode = render_mode
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self.random_n_sheep = random_n_sheep # if True, randomise n_sheep each reset
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# Override class-default reward weights / shape with per-instance config
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# so sweeps can ship configs into subprocess envs via pickled make_env.
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if reward_cfg:
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for k, v in reward_cfg.items():
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if not hasattr(self.__class__, k):
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raise ValueError(f"unknown reward_cfg key: {k}")
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setattr(self, k, v)
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# Fixed 16-dim observation regardless of n_sheep:
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# dog_pos(2) + rel_com(2) + rel_far1(2) + rel_far2(2) + rel_far3(2)
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# + com_to_pen(2) + far1_to_pen(2) + radius(1) + frac_penned(1)
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self.observation_space = spaces.Box(
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low=-np.inf, high=np.inf, shape=(16,), dtype=np.float32
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)
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# Action: desired velocity (vx, vy) ∈ [-1, 1]², scaled by DOG_SPEED
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self.action_space = spaces.Box(
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low=-1.0, high=1.0, shape=(2,), dtype=np.float32
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)
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# Runtime state (populated by reset)
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self._step_count = 0
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self._prev_penned = 0
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self._prev_pen_dist_sum = 0.0
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self.dog_pos = np.zeros(2, dtype=np.float32)
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self.sheep_pos = np.zeros((self.MAX_SHEEP, 2), dtype=np.float32)
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self.penned = np.ones(self.MAX_SHEEP, dtype=bool)
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self.wander_ang = np.zeros(self.MAX_SHEEP, dtype=np.float32)
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self._fig = None
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# ------------------------------------------------------------------
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# Curriculum interface
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# ------------------------------------------------------------------
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def set_n_sheep(self, n: int):
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"""Advance curriculum difficulty; takes effect on next reset()."""
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assert 1 <= n <= self.MAX_SHEEP
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self.n_sheep = n
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# ------------------------------------------------------------------
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# Gymnasium API
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# ------------------------------------------------------------------
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def reset(self, seed=None, options=None):
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super().reset(seed=seed)
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self._step_count = 0
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self._prev_penned = 0
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if self.random_n_sheep:
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self.n_sheep = int(self.np_random.integers(1, self.MAX_SHEEP + 1))
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# Active sheep (0 .. n_sheep-1): random non-pen positions
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self.sheep_pos[:] = self.PEN_CENTER
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self.penned[:] = True
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# Spawn first sheep anywhere; subsequent sheep clustered around it
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# so boid cohesion (active within 8m) keeps the flock together.
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# Without clustering, sheep can start 25m apart and never coalesce —
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# task becomes intractable for n_sheep ≥ 2.
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placed = 0
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cluster_center = None
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radius = self.INIT_CLUSTER_RADIUS
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while placed < self.n_sheep:
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if placed == 0 or radius is None:
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p = self.np_random.uniform(-12.0, 12.0, size=(2,)).astype(np.float32)
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else:
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offset = self.np_random.uniform(-radius, radius, size=(2,))
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p = (cluster_center + offset).astype(np.float32)
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p = np.clip(p, -12.0, 12.0)
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if not self._in_pen(p):
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self.sheep_pos[placed] = p
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self.penned[placed] = False
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if placed == 0:
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cluster_center = p.copy()
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placed += 1
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# Dog: 50% of resets start already behind the flock (anti-pen side,
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# within flee range) to give early training aligned experiences.
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# Use the flock COM as the reference (not sheep[0]) so the bias
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# generalizes from 1-sheep to multi-sheep without putting the dog
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# in front of or inside the flock.
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if self.np_random.random() < 0.5:
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active_pts = self.sheep_pos[:self.n_sheep][~self.penned[:self.n_sheep]]
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ref = active_pts.mean(axis=0) if len(active_pts) else self.sheep_pos[0]
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away = ref - self.PEN_CENTER
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d = float(np.linalg.norm(away))
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if d > 0.1:
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away = away / d
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offset = away * self.np_random.uniform(2.0, self.FLEE_DIST * 0.8)
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self.dog_pos = np.clip(
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(ref + offset).astype(np.float32), -self.FIELD, self.FIELD
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)
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else:
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self.dog_pos = self.np_random.uniform(
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-self.FIELD * 0.8, self.FIELD * 0.8, size=(2,)
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).astype(np.float32)
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self.wander_ang = self.np_random.uniform(
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-np.pi, np.pi, size=(self.MAX_SHEEP,)
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).astype(np.float32)
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# Initialise per-sheep pen-distance sum for progress reward
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active = ~self.penned[:self.n_sheep]
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target = self.PEN_ENTRY if self.ENTRY_AWARE else self.PEN_CENTER
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if active.any():
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self._prev_pen_dist_sum = float(
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np.linalg.norm(
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self.sheep_pos[:self.n_sheep][active] - target, axis=1
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).sum()
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)
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com0 = self.sheep_pos[:self.n_sheep][active].mean(axis=0)
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self._prev_radius = float(
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np.linalg.norm(self.sheep_pos[:self.n_sheep][active] - com0, axis=1).max()
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)
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else:
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self._prev_pen_dist_sum = 0.0
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self._prev_radius = 0.0
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return self._obs(), {}
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def step(self, action):
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self._step_count += 1
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act = np.clip(np.asarray(action, dtype=np.float32), -1.0, 1.0)
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old_dog = self.dog_pos.copy()
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new_dog = np.clip(
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self.dog_pos + act * self.DOG_SPEED * self.DT,
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-self.FIELD, self.FIELD
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)
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# Pen wall collision — mirrors Webots geometry. West (x=PEN_X[0]) and
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# east (x=PEN_X[1]) walls block the dog within the pen's y-range.
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# North face (y=PEN_Y[1]=-8) is open. South is the field edge.
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px0, px1 = self.PEN_X
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py0, py1 = self.PEN_Y
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if py0 < new_dog[1] < py1:
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if old_dog[0] < px0 <= new_dog[0]:
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new_dog[0] = px0 - 1e-3
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elif old_dog[0] > px0 >= new_dog[0]:
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new_dog[0] = px0 + 1e-3
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if old_dog[0] > px1 >= new_dog[0]:
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new_dog[0] = px1 + 1e-3
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elif old_dog[0] < px1 <= new_dog[0]:
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new_dog[0] = px1 - 1e-3
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self.dog_pos = new_dog.astype(np.float32)
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for i in range(self.n_sheep):
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if self.penned[i]:
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continue
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self.sheep_pos[i] = self._step_sheep(i)
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if self._in_pen(self.sheep_pos[i]):
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self.penned[i] = True
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n_penned = int(self.penned[:self.n_sheep].sum())
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newly_penned = n_penned - self._prev_penned
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self._prev_penned = n_penned
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reward, rcomps = self._reward(n_penned, newly_penned, act)
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terminated = n_penned == self.n_sheep
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truncated = self._step_count >= self.max_steps
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info = {"n_penned": n_penned, "n_sheep": self.n_sheep,
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"rcomps": rcomps}
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if self.render_mode == "human":
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self.render()
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return self._obs(), float(reward), terminated, truncated, info
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def render(self):
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import matplotlib.pyplot as plt
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import matplotlib.patches as mpatches
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if self._fig is None:
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plt.ion()
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self._fig, self._ax = plt.subplots(figsize=(6, 6))
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ax = self._ax
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ax.clear()
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ax.set_xlim(-16, 16); ax.set_ylim(-16, 16)
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ax.set_aspect("equal"); ax.set_facecolor("#dcedc8")
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ax.add_patch(mpatches.Rectangle(
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(-15, -15), 30, 30, fill=False, edgecolor="#795548", linewidth=2
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))
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pw = self.PEN_X[1] - self.PEN_X[0]
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ph = self.PEN_Y[1] - self.PEN_Y[0]
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ax.add_patch(mpatches.Rectangle(
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(self.PEN_X[0], self.PEN_Y[0]), pw, ph,
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facecolor="#ffe082", edgecolor="#795548", linewidth=2
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))
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ax.text(11.5, -11.5, "pen", ha="center", va="center",
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fontsize=8, color="#795548")
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com, radius, _ = self._flock_stats()
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ax.add_patch(plt.Circle(com, radius, color="steelblue",
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fill=False, linestyle="--", linewidth=1))
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ax.plot(*com, "+", color="steelblue", markersize=10)
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for i in range(self.n_sheep):
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if i >= self.n_sheep:
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continue
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color = "deeppink" if self.penned[i] else "white"
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ax.plot(*self.sheep_pos[i], "o", color=color, markersize=11,
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markeredgecolor="#555", markeredgewidth=1.5)
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ax.plot(*self.dog_pos, "s", color="#4e342e", markersize=13,
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markeredgecolor="black", markeredgewidth=1.5)
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ax.set_title(
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f"step {self._step_count} | "
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f"penned {int(self.penned[:self.n_sheep].sum())}/{self.n_sheep} | "
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f"r={radius:.1f}m",
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fontsize=11
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)
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self._fig.canvas.draw()
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self._fig.canvas.flush_events()
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plt.pause(0.001)
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def close(self):
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if self._fig is not None:
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import matplotlib.pyplot as plt
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plt.close(self._fig)
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self._fig = None
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# ------------------------------------------------------------------
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# Internals
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# ------------------------------------------------------------------
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def _in_pen(self, pos: np.ndarray) -> bool:
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return (self.PEN_X[0] < pos[0] < self.PEN_X[1] and
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self.PEN_Y[0] < pos[1] < self.PEN_Y[1])
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def _flock_stats(self):
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"""Return (COM, radius, mean_dispersion) over active sheep."""
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active_mask = ~self.penned[:self.n_sheep]
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if not active_mask.any():
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return self.PEN_CENTER.copy(), 0.0, 0.0
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pts = self.sheep_pos[:self.n_sheep][active_mask]
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com = pts.mean(axis=0)
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dists = np.linalg.norm(pts - com, axis=1)
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return com, float(dists.max()), float(dists.mean())
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def _obs(self) -> np.ndarray:
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com, radius, _ = self._flock_stats()
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active_mask = ~self.penned[:self.n_sheep]
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if active_mask.any():
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pts = self.sheep_pos[:self.n_sheep][active_mask]
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dists = np.linalg.norm(pts - com, axis=1)
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sorted_idx = np.argsort(dists)[::-1] # farthest first
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# Top-3 stragglers; pad with COM when fewer active sheep exist
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def nth(n):
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return pts[sorted_idx[n]] if len(sorted_idx) > n else com
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far1, far2, far3 = nth(0), nth(1), nth(2)
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else:
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far1 = far2 = far3 = self.PEN_CENTER.copy()
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S = self.FIELD
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D = 2 * self.FIELD
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# far1/far2/far3 expressed relative to COM, not dog.
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# For 1 sheep: far1-COM = far2-COM = far3-COM = [0,0] → cleanly ignorable.
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# For 3+ sheep: non-zero vectors tell the dog where each straggler is
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# within the group, without conflicting with weights trained on 1 sheep.
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# Pen reference for the policy. Aligned with the reward target so the
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# policy isn't forced to learn an implicit offset between what it sees
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# ("pen is here") and what it's rewarded for ("get sheep close to here").
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pen_ref = self.PEN_ENTRY if self.ENTRY_AWARE else self.PEN_CENTER
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return np.array([
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self.dog_pos[0] / S, self.dog_pos[1] / S,
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(com[0] - self.dog_pos[0]) / D, (com[1] - self.dog_pos[1]) / D,
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(far1[0] - com[0]) / D, (far1[1] - com[1]) / D,
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(far2[0] - com[0]) / D, (far2[1] - com[1]) / D,
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(far3[0] - com[0]) / D, (far3[1] - com[1]) / D,
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(pen_ref[0] - com[0]) / D, (pen_ref[1] - com[1]) / D,
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(pen_ref[0] - far1[0]) / D, (pen_ref[1] - far1[1]) / D,
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radius / D,
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active_mask.sum() / self.n_sheep,
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], dtype=np.float32)
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def _reward(self, n_penned: int, newly_penned: int, action: np.ndarray):
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active = ~self.penned[:self.n_sheep]
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# Per-sheep progress toward pen: fires whenever any sheep moves closer.
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# Naturally rewards keeping the flock together and pushing toward pen:
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# dog behind flock → all sheep flee toward pen → all contribute positive reward.
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# Dog from wrong side → sheep scatter away from pen → negative reward.
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target = self.PEN_ENTRY if self.ENTRY_AWARE else self.PEN_CENTER
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if active.any():
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pen_dists = np.linalg.norm(
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self.sheep_pos[:self.n_sheep][active] - target, axis=1
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)
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cur_sum = float(pen_dists.sum())
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r_progress = (self._prev_pen_dist_sum - cur_sum) * self.W_PER_SHEEP
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self._prev_pen_dist_sum = cur_sum
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else:
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r_progress = 0.0
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com, _, _ = self._flock_stats()
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com_dist = float(np.linalg.norm(com - target))
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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 = (target - 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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if self.ALIGN_SHAPE == "standoff":
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IDEAL = 0.5 * (self.SEPARATION_DIST + self.FLEE_DIST)
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HALF = self.FLEE_DIST - IDEAL
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proximity = max(0.0, 1.0 - abs(d_dog_com - IDEAL) / HALF)
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else: # "near"
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proximity = max(0.0, 1.0 - d_dog_com / self.FLEE_DIST)
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move_gate = (min(1.0, float(np.linalg.norm(action)))
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if self.ALIGN_GATED else 1.0)
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alignment = cosine * proximity * move_gate * self.W_ALIGN
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else:
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alignment = 0.0
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# Wall-touch penalty: distance-based gradient covering all 3 solid pen
|
||
# walls (west, east, south). Linearly ramps from 0 at buffer edge to
|
||
# W_WALL_TOUCH at the wall surface — gives the agent a smooth signal
|
||
# to avoid pinning sheep against walls.
|
||
if self.W_WALL_TOUCH and active.any():
|
||
pts = self.sheep_pos[:self.n_sheep][active]
|
||
px0, px1 = self.PEN_X
|
||
py0, py1 = self.PEN_Y
|
||
buf = self.WALL_TOUCH_BUFFER
|
||
far = buf + 1.0
|
||
d_w = np.where((pts[:, 0] < px0) & (pts[:, 1] > py0) & (pts[:, 1] < py1),
|
||
px0 - pts[:, 0], far)
|
||
d_e = np.where((pts[:, 0] > px1) & (pts[:, 1] > py0) & (pts[:, 1] < py1),
|
||
pts[:, 0] - px1, far)
|
||
d_s = np.where((pts[:, 1] < py0) & (pts[:, 0] > px0) & (pts[:, 0] < px1),
|
||
py0 - pts[:, 1], far)
|
||
d_min = np.minimum(np.minimum(d_w, d_e), d_s)
|
||
penalties = np.maximum(0.0, 1.0 - d_min / buf) * self.W_WALL_TOUCH
|
||
r_wall_touch = -float(penalties.sum())
|
||
else:
|
||
r_wall_touch = 0.0
|
||
|
||
# Compactness shaping: reward decreases in flock radius (active sheep only)
|
||
if self.W_COMPACT and active.any():
|
||
cur_radius = float(np.linalg.norm(
|
||
self.sheep_pos[:self.n_sheep][active] - com, axis=1
|
||
).max())
|
||
r_compact = (self._prev_radius - cur_radius) * self.W_COMPACT
|
||
self._prev_radius = cur_radius
|
||
else:
|
||
r_compact = 0.0
|
||
|
||
r_pen_bonus = newly_penned * self.W_PEN_BONUS
|
||
r_step_cost = -self.W_STEP_COST
|
||
r_complete = self.W_COMPLETE if n_penned == self.n_sheep else 0.0
|
||
reward = (r_progress + alignment + r_compact + r_wall_touch
|
||
+ r_pen_bonus + r_step_cost + r_complete)
|
||
rcomps = {
|
||
"progress": float(r_progress),
|
||
"alignment": float(alignment),
|
||
"compact": float(r_compact),
|
||
"wall_touch": float(r_wall_touch),
|
||
"pen_bonus": float(r_pen_bonus),
|
||
"step_cost": float(r_step_cost),
|
||
"complete": float(r_complete),
|
||
}
|
||
return reward, rcomps
|
||
|
||
def _step_sheep(self, i: int) -> np.ndarray:
|
||
"""Apply one timestep of boid dynamics to sheep i (mirrors sheep.py)."""
|
||
old_pos = self.sheep_pos[i].copy() # saved for pen wall collision check
|
||
pos = old_pos.copy()
|
||
fx, fy = 0.0, 0.0
|
||
fleeing = False
|
||
|
||
# Flee from dog — quadratic ramp
|
||
diff = self.dog_pos - pos
|
||
dist = float(np.linalg.norm(diff))
|
||
if 0.01 < dist < self.FLEE_DIST:
|
||
t = 1.0 - dist / self.FLEE_DIST
|
||
s = t * t * 5.0
|
||
fx -= (diff[0] / dist) * s
|
||
fy -= (diff[1] / dist) * s
|
||
fleeing = True
|
||
|
||
# Separation (inverse-distance) + Cohesion
|
||
cx, cy, cn = 0.0, 0.0, 0
|
||
for j in range(self.n_sheep):
|
||
if j == i or self.penned[j]:
|
||
continue
|
||
dv = self.sheep_pos[j] - pos
|
||
dj = float(np.linalg.norm(dv))
|
||
if 0.3 < dj < self.COHESION_DIST:
|
||
cx += self.sheep_pos[j][0]
|
||
cy += self.sheep_pos[j][1]
|
||
cn += 1
|
||
if 0.05 < dj < self.SEPARATION_DIST:
|
||
push = (self.SEPARATION_DIST - dj) / dj
|
||
fx -= (dv[0] / dj) * push * 2.5
|
||
fy -= (dv[1] / dj) * push * 2.5
|
||
if cn > 0:
|
||
w = 0.08 if fleeing else 0.15
|
||
fx += (cx / cn - pos[0]) * w
|
||
fy += (cy / cn - pos[1]) * w
|
||
|
||
# Wall avoidance
|
||
m, F = self.WALL_MARGIN, self.FIELD
|
||
if pos[0] < -F + m: fx += ((-F + m - pos[0]) / m) * 6.0
|
||
if pos[0] > F - m: fx -= ((pos[0] - (F - m)) / m) * 6.0
|
||
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
|
||
|
||
|
||
# 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
|
||
if pos[0] < -F + HS and fx < 0: fx = 0.0
|
||
if pos[0] > F - HS and fx > 0: fx = 0.0
|
||
if pos[1] < -F + HS and fy < 0: fy = 0.0
|
||
if pos[1] > F - HS and fy > 0: fy = 0.0
|
||
|
||
# Wander — suppressed while fleeing
|
||
if not fleeing:
|
||
if self.np_random.random() < 0.02:
|
||
self.wander_ang[i] += float(self.np_random.uniform(-0.6, 0.6))
|
||
fx += float(np.cos(self.wander_ang[i])) * 0.5
|
||
fy += float(np.sin(self.wander_ang[i])) * 0.5
|
||
|
||
# Integrate
|
||
force = np.array([fx, fy])
|
||
mag = float(np.linalg.norm(force))
|
||
if mag > 0.01:
|
||
top_speed = self.SHEEP_FLEE_V if fleeing else self.SHEEP_WANDER_V
|
||
speed = min(top_speed, mag * 0.3)
|
||
pos = np.clip(pos + (force / mag) * speed * self.DT,
|
||
-self.FIELD, self.FIELD)
|
||
|
||
# Pen solid wall collision — mirrors Webots geometry.
|
||
# The pen has THREE solid walls: west (x=PEN_X[0]), east (x=PEN_X[1]),
|
||
# south (y=PEN_Y[0]). The NORTH face (y=PEN_Y[1]=-8) is the open entrance.
|
||
# Sheep may only enter through the north face; crossing a solid wall is blocked.
|
||
px0, px1 = self.PEN_X[0], self.PEN_X[1]
|
||
py0, py1 = self.PEN_Y[0], self.PEN_Y[1]
|
||
entered_from_north = (
|
||
old_pos[1] >= py1 and pos[1] < py1 and px0 < pos[0] < px1
|
||
)
|
||
if not entered_from_north:
|
||
# Block crossing through west wall from outside
|
||
if old_pos[0] < px0 <= pos[0] and py0 < pos[1] < py1:
|
||
pos = np.array([px0 - 1e-3, pos[1]], dtype=np.float32)
|
||
# Block crossing through east wall from outside
|
||
if old_pos[0] > px1 >= pos[0] and py0 < pos[1] < py1:
|
||
pos = np.array([px1 + 1e-3, pos[1]], dtype=np.float32)
|
||
|
||
return pos.astype(np.float32)
|