chore: min/max of inputs

scripts/extract_observation_bounds.py

@@ -0,0 +1,138 @@
+#!/usr/bin/env python3
+"""Empirically extract observation bounds (focused on joint velocities).
+
+This script creates a MuJoCo environment using the project's factory and
+randomly samples actions to discover observed maxima for selected
+observation keys (joint_velocity, joint_position, joint_actuator_force).
+
+Usage:
+  python scripts/extract_observation_bounds.py \
+    --morphology configs/morphology/3_arms.yaml --num-steps 5000 --seed 42
+
+If `--morphology` is omitted the default `MorphologyConfig()` is used.
+"""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+import yaml
+import numpy as np
+
+from brittle_star_project import BrittleStarEnvFactory, BrittleStarEnv, Backend
+from brittle_star_project.environment.env_config import (
+    MorphologyConfig,
+    ArenaConfig,
+    EnvConfig,
+)
+
+
+def load_morphology(path: str | None) -> MorphologyConfig:
+    if path is None:
+        return MorphologyConfig()
+    p = Path(path)
+    if not p.exists():
+        raise FileNotFoundError(f"Morphology file not found: {p}")
+    with open(p, "r") as f:
+        data = yaml.safe_load(f) or {}
+    return MorphologyConfig(**data)
+
+
+def _extract_observations(state):
+    # Under different backends the returned state may be a dict or an object
+    obs = getattr(state, "observations", None)
+    if obs is None and isinstance(state, dict):
+        obs = state.get("observations", state)
+    return obs
+
+
+def find_empirical_bounds(
+    morph_cfg: MorphologyConfig,
+    arena_cfg: ArenaConfig,
+    env_cfg: EnvConfig,
+    num_steps: int = 5000,
+    seed: int = 42,
+) -> None:
+    factory = BrittleStarEnvFactory()
+    raw_env = factory.create_environment(Backend.MJC, morph_cfg, arena_cfg, env_cfg)
+    env = BrittleStarEnv(raw_env, backend=Backend.MJC, config=env_cfg, morphology_config=morph_cfg)
+
+    # Initial reset
+    state = env.reset(seed=seed)
+
+    # Determine action bounds
+    action_space = getattr(raw_env, "action_space", None)
+    if action_space is None:
+        raise RuntimeError("Environment missing `action_space`; cannot sample actions.")
+
+    action_low = np.asarray(action_space.low, dtype=np.float32)
+    action_high = np.asarray(action_space.high, dtype=np.float32)
+    action_shape = action_low.shape
+
+    # Track maximum absolute observed values
+    tracked_keys = ["joint_velocity", "joint_position", "joint_actuator_force"]
+    max_observed = {k: 0.0 for k in tracked_keys}
+
+    # Include observation at reset
+    obs0 = _extract_observations(state)
+    if isinstance(obs0, dict):
+        for k in tracked_keys:
+            if k in obs0:
+                max_observed[k] = max(max_observed[k], float(np.max(np.abs(np.asarray(obs0[k])))))
+
+    rng = np.random.RandomState(seed)
+    for i in range(num_steps):
+        u = rng.uniform(size=action_shape)
+        action = action_low + (action_high - action_low) * u
+
+        # Provide a numpy RNG to the env step; wrapper will pass it if accepted.
+        step_out = env.step(state=state, action=action, rng=env.make_rng(seed + i + 1))
+
+        # Unpack next state from common return conventions
+        if hasattr(step_out, "state"):
+            next_state = step_out.state
+        elif isinstance(step_out, (tuple, list)) and len(step_out) >= 1:
+            next_state = step_out[0]
+        else:
+            next_state = step_out
+
+        obs = _extract_observations(next_state)
+        if isinstance(obs, dict):
+            for k in tracked_keys:
+                if k in obs:
+                    val = float(np.max(np.abs(np.asarray(obs[k]))))
+                    if val > max_observed[k]:
+                        max_observed[k] = val
+
+        state = next_state
+
+    # Print recommended bounds with a 20% safety margin
+    print("\n--- Recommended Observation Bounds (20% margin) ---")
+    for k, v in max_observed.items():
+        if v == 0.0:
+            print(f"{k}: observed max 0.0 (increase sampling or inspect env)")
+        else:
+            safe = v * 1.2
+            print(f"{k}: [-{safe:.6f}, {safe:.6f}]  (observed max: {v:.6f})")
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--morphology", type=str, default=None, help="Path to morphology YAML (optional)"
+    )
+    parser.add_argument(
+        "--num-steps", type=int, default=5000, help="Number of random steps to sample"
+    )
+    parser.add_argument("--seed", type=int, default=42, help="RNG seed")
+    args = parser.parse_args()
+
+    morph_cfg = load_morphology(args.morphology)
+    arena_cfg = ArenaConfig()
+    env_cfg = EnvConfig()
+
+    find_empirical_bounds(morph_cfg, arena_cfg, env_cfg, num_steps=args.num_steps, seed=args.seed)
+
+
+if __name__ == "__main__":
+    main()

src/brittle_star_project/environment/env_config.py

@@ -66,10 +66,15 @@ class EnvConfig:
 class ObservationBoundsConfig:
     """Physical observation bounds for deterministic min-max normalization."""
 
-    # TODO Inspect empirically observed ranges and update these bounds as needed.
+    # Empirical testing based on the extract_observation_bounds.py script run for 1.000.000 steps
-    joint_position: list[float] = field(default_factory=lambda: [-3.14, 3.14])
+
-    joint_velocity: list[float] = field(default_factory=lambda: [-20.0, 20.0])
+    # Based on max. ctrlrange (0.78539816339744828) in XML, but empirical testing went slightly over
-    joint_actuator_force: list[float] = field(default_factory=lambda: [-5.0, 5.0])
+    joint_position: list[float] = field(default_factory=lambda: [-0.8, 0.8])
+    # Empirical testing showed max. 3.22, adding buffer to be safe
+    joint_velocity: list[float] = field(default_factory=lambda: [-5.0, 5.0])
+    # Based on max. forceRange in XML, verified with empirical testing
+    joint_actuator_force: list[float] = field(default_factory=lambda: [-3.75, 3.75])
+    # Based on intuition and reasoning
     segment_contact: list[float] = field(default_factory=lambda: [0.0, 1.0])
     unit_xy_direction_to_target: list[float] = field(default_factory=lambda: [-1.0, 1.0])
     disk_z_tilt: list[float] = field(default_factory=lambda: [0.0, 3.141592653589793])