feat(training): release V0.8 自调参 Agent

training_server/tuning/scoring.py

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+"""Stable, reward-weight-independent evaluation scoring."""
+
+from __future__ import annotations
+
+import math
+from collections.abc import Mapping
+from typing import Any
+
+DEFAULT_OBJECTIVE_WEIGHTS = {
+    "velocity_tracking": 0.35,
+    "action_smoothness": 0.20,
+    "posture_stability": 0.15,
+    "fall_avoidance": 0.15,
+    "foot_slip": 0.10,
+    "energy": 0.05,
+}
+
+REQUIRED_METRICS = {
+    "linear_velocity_rmse",
+    "angular_velocity_rmse",
+    "mean_action_acc",
+    "orientation_error",
+    "fall_rate",
+    "slip_velocity",
+    "mechanical_power",
+}
+
+PHYSICAL_FLOORS = {
+    "linear_velocity_rmse": 0.10,
+    "angular_velocity_rmse": 0.10,
+    "mean_action_acc": 0.01,
+    "orientation_error": 0.05,
+    "fall_rate": 0.02,
+    "slip_velocity": 0.05,
+    "mechanical_power": 10.0,
+}
+
+
+class EvaluationError(ValueError):
+    pass
+
+
+def validate_objective_weights(value: Any) -> dict[str, float]:
+    if not isinstance(value, Mapping) or set(value) != set(DEFAULT_OBJECTIVE_WEIGHTS):
+        raise EvaluationError("objectiveWeights 必须完整包含六个目标")
+    result: dict[str, float] = {}
+    for key in DEFAULT_OBJECTIVE_WEIGHTS:
+        raw = value[key]
+        if isinstance(raw, bool) or not isinstance(raw, (int, float)):
+            raise EvaluationError(f"objectiveWeights.{key} 必须是数值")
+        number = float(raw)
+        if not math.isfinite(number) or number < 0.0 or number > 1.0:
+            raise EvaluationError(f"objectiveWeights.{key} 必须在 0–1 之间")
+        result[key] = number
+    if not math.isclose(sum(result.values()), 1.0, abs_tol=1.0e-6):
+        raise EvaluationError("objectiveWeights 总和必须为 1")
+    return result
+
+
+def validate_metrics(value: Any) -> dict[str, float]:
+    if not isinstance(value, Mapping):
+        raise EvaluationError("metrics 必须是对象")
+    missing = REQUIRED_METRICS - set(value)
+    if missing:
+        raise EvaluationError(f"metrics 缺少：{', '.join(sorted(missing))}")
+    result: dict[str, float] = {}
+    for key in REQUIRED_METRICS:
+        raw = value[key]
+        if isinstance(raw, bool) or not isinstance(raw, (int, float)):
+            raise EvaluationError(f"metrics.{key} 必须是数值")
+        number = float(raw)
+        if not math.isfinite(number) or number < 0.0:
+            raise EvaluationError(f"metrics.{key} 必须是非负有限数值")
+        result[key] = number
+    if result["fall_rate"] > 1.0:
+        raise EvaluationError("metrics.fall_rate 必须在 0–1 之间")
+    return result
+
+
+def _improvement(baseline: Mapping[str, float], current: Mapping[str, float], key: str) -> float:
+    scale = max(abs(baseline[key]), PHYSICAL_FLOORS[key])
+    return max(-1.0, min(1.0, (baseline[key] - current[key]) / scale))
+
+
+def score_evaluation(
+    baseline_value: Any,
+    current_value: Any,
+    objective_weights: Any = DEFAULT_OBJECTIVE_WEIGHTS,
+) -> dict[str, Any]:
+    baseline = validate_metrics(baseline_value)
+    current = validate_metrics(current_value)
+    weights = validate_objective_weights(objective_weights)
+    components = {
+        "velocity_tracking": 0.8 * _improvement(baseline, current, "linear_velocity_rmse")
+        + 0.2 * _improvement(baseline, current, "angular_velocity_rmse"),
+        "action_smoothness": _improvement(baseline, current, "mean_action_acc"),
+        "posture_stability": _improvement(baseline, current, "orientation_error"),
+        "fall_avoidance": _improvement(baseline, current, "fall_rate"),
+        "foot_slip": _improvement(baseline, current, "slip_velocity"),
+        "energy": _improvement(baseline, current, "mechanical_power"),
+    }
+    tracking_limit = max(
+        baseline["linear_velocity_rmse"] * 1.05, baseline["linear_velocity_rmse"] + 1.0e-6
+    )
+    eligible = (
+        current["fall_rate"] <= baseline["fall_rate"] + 0.02
+        and current["linear_velocity_rmse"] <= tracking_limit
+    )
+    total = sum(weights[key] * components[key] for key in weights)
+    if not eligible:
+        total = min(total, -1.0)
+    return {
+        "eligible": eligible,
+        "score": total,
+        "components": components,
+        "metrics": current,
+        "baselineMetrics": baseline,
+    }