Johnny Fernandes
354 lines
14 KiB
Python
354 lines
14 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 is always sized for MAX_SHEEP (currently 5) regardless of
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how many sheep are active. Inactive slots are pre-penned at the pen
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centre with flag=1. This keeps the model input dimension fixed across
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curriculum stages so VecNormalize statistics are preserved throughout.
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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 = 5
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FIELD = 15.0 # half-size; positions ∈ [-FIELD, FIELD]
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PEN_X = (10.0, 13.0) # quarantine pen x bounds
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PEN_Y = (-15.0, -8.0) # quarantine pen y bounds
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PEN_CENTER = np.array([11.5, -11.5], dtype=np.float32)
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# -----------------------------------------------------------------------
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# Dynamics — calibrated to match Webots robot specs
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# wheel radius 0.031 m; sheep FLEE_SPEED 20 rad/s → 0.62 m/s
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# wheel radius 0.038 m; dog maxVelocity 70 rad/s → 2.66 m/s
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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
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# -----------------------------------------------------------------------
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W_ALIGN = 0.4 # dense: dog on anti-pen side of each active sheep
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W_SHAPING = 0.5 # dense: mean sheep distance to pen
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W_APPROACH = 0.1 # dense: dog within flee range of nearest sheep
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W_PEN_BONUS = 5.0 # sparse: per sheep successfully penned
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W_COMPLETE = 20.0 # bonus when ALL active sheep are penned
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W_STEP_COST = 0.002 # penalty per step (encourages efficiency)
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def __init__(self, n_sheep: int = 1, max_steps: int = 2000,
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render_mode: str = 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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# Observation: dog(x,y) + MAX_SHEEP×sheep(x,y) + MAX_SHEEP×penned
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# Fixed size across all curriculum stages.
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obs_dim = 2 + 2 * self.MAX_SHEEP + self.MAX_SHEEP
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self.observation_space = spaces.Box(
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low=-1.0, high=1.0, shape=(obs_dim,), 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.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 # lazy matplotlib figure
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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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# 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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placed = 0
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while placed < self.n_sheep:
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p = self.np_random.uniform(-12.0, 12.0, size=(2,)).astype(np.float32)
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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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placed += 1
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# Dog: 50 % of the time start already on the anti-pen side of the
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# nearest sheep (within flee range) so early training gets aligned
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# starts; the other 50 % is fully random to ensure generalisation.
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if self.np_random.random() < 0.5:
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# Place dog behind the first active sheep relative to the pen
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ref = self.sheep_pos[0]
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away = ref - self.PEN_CENTER # sheep→anti-pen
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dist = float(np.linalg.norm(away))
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if dist > 0.1:
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away = away / dist
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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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# Inactive slots (n_sheep .. MAX_SHEEP-1): already at pen centre, penned=True
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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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return self._obs(), {}
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def step(self, action):
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self._step_count += 1
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# Move dog — clip each axis independently so the agent can idle
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act = np.clip(np.asarray(action, dtype=np.float32), -1.0, 1.0)
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self.dog_pos = 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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# Step sheep dynamics
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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 = self._reward(n_penned, newly_penned)
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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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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)
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ax.set_ylim(-16, 16)
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ax.set_aspect("equal")
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ax.set_facecolor("#dcedc8")
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# Field boundary
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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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# Pen
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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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# Sheep
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for i in range(self.MAX_SHEEP):
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if i >= self.n_sheep:
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continue # inactive slot — not shown
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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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# Dog
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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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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 _obs(self) -> np.ndarray:
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scale = 1.0 / self.FIELD
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return np.concatenate([
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self.dog_pos * scale, # 2
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(self.sheep_pos * scale).flatten(), # 2 * MAX_SHEEP
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self.penned.astype(np.float32), # MAX_SHEEP
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]).astype(np.float32)
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def _reward(self, n_penned: int, newly_penned: int) -> float:
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active_mask = ~self.penned[:self.n_sheep]
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if active_mask.any():
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active_pos = self.sheep_pos[:self.n_sheep][active_mask]
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dists_pen = np.linalg.norm(active_pos - self.PEN_CENTER, axis=1)
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dists_dog = np.linalg.norm(active_pos - self.dog_pos, axis=1)
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# Sheep-to-pen shaping
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shaping = -(dists_pen.mean() / (2 * self.FIELD))
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# Approach: dog penalised for being far from nearest sheep
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approach = -(dists_dog.min() / (2 * self.FIELD))
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# Alignment: reward dog for being on the anti-pen side of each sheep.
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# When the dog is opposite the pen relative to a sheep, that sheep
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# flees toward the pen. Score ∈ [-1, 1] per sheep, weighted by
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# a proximity gate so only nearby dogs count.
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align_scores = []
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for s_pos, d_pen, d_dog in zip(active_pos, dists_pen, dists_dog):
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if d_pen < 0.1 or d_dog < 0.1:
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continue
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pen_dir = (self.PEN_CENTER - s_pos) / d_pen # sheep → pen
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dog_dir = (self.dog_pos - s_pos) / d_dog # sheep → dog
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# cos(angle): +1 → dog behind sheep, -1 → dog on pen side
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cosine = -float(np.dot(pen_dir, dog_dir))
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# gate: full credit inside flee range, fades beyond
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proximity = max(0.0, 1.0 - d_dog / self.FLEE_DIST)
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align_scores.append(cosine * proximity)
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alignment = float(np.mean(align_scores)) if align_scores else 0.0
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else:
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shaping = approach = alignment = 0.0
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reward = shaping * self.W_SHAPING
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reward += approach * self.W_APPROACH
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reward += alignment * self.W_ALIGN
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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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reward += self.W_COMPLETE
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return reward
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def _step_sheep(self, i: int) -> np.ndarray:
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"""Apply one timestep of boid dynamics to sheep i."""
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pos = self.sheep_pos[i].copy()
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fx, fy = 0.0, 0.0
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fleeing = False
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# Flee from dog — quadratic ramp (mirrors sheep.py)
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diff = self.dog_pos - pos
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dist = float(np.linalg.norm(diff))
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if 0.01 < dist < self.FLEE_DIST:
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t = 1.0 - dist / self.FLEE_DIST
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s = t * t * 5.0
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fx -= (diff[0] / dist) * s
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fy -= (diff[1] / dist) * s
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fleeing = True
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# Separation (inverse-distance) + Cohesion
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cx, cy, cn = 0.0, 0.0, 0
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for j in range(self.n_sheep):
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if j == i or self.penned[j]:
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continue
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dv = self.sheep_pos[j] - pos
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dj = float(np.linalg.norm(dv))
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if 0.3 < dj < self.COHESION_DIST:
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cx += self.sheep_pos[j][0]
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cy += self.sheep_pos[j][1]
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cn += 1
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if 0.05 < dj < self.SEPARATION_DIST:
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push = (self.SEPARATION_DIST - dj) / dj
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fx -= (dv[0] / dj) * push * 2.5
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fy -= (dv[1] / dj) * push * 2.5
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if cn > 0:
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w = 0.08 if fleeing else 0.15
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fx += (cx / cn - pos[0]) * w
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fy += (cy / cn - pos[1]) * w
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# Wall avoidance
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m, F = self.WALL_MARGIN, self.FIELD
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if pos[0] < -F + m: fx += ((-F + m - pos[0]) / m) * 6.0
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if pos[0] > F - m: fx -= ((pos[0] - (F - m)) / m) * 6.0
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if pos[1] < -F + m: fy += ((-F + m - pos[1]) / m) * 6.0
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if pos[1] > F - m: fy -= ((pos[1] - (F - m)) / m) * 6.0
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# Wander — suppressed while fleeing
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if not fleeing:
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if self.np_random.random() < 0.02:
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self.wander_ang[i] += float(self.np_random.uniform(-0.6, 0.6))
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fx += float(np.cos(self.wander_ang[i])) * 0.5
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fy += float(np.sin(self.wander_ang[i])) * 0.5
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# Integrate
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force = np.array([fx, fy])
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mag = float(np.linalg.norm(force))
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if mag > 0.01:
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top_speed = self.SHEEP_FLEE_V if fleeing else self.SHEEP_WANDER_V
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speed = min(top_speed, mag * 0.3)
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pos = np.clip(pos + (force / mag) * speed * self.DT,
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-self.FIELD, self.FIELD)
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return pos.astype(np.float32)
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