feat(training): release V0.9.1 避障训练与基础策略迁移

training_server/tuning/obstacle_scoring.py

@@ -0,0 +1,130 @@
+"""Fixed obstacle protocol; objective measurements never consume training rewards."""
+
+import math
+from statistics import fmean
+
+from task_config import build_terrain_layout, validate_task_config
+
+from .schema import OBSTACLE_TASK
+from .scoring import EvaluationError
+
+SEEDS = (101, 202, 303)
+STEPS = 1000
+WEIGHTS = {"success": 0.4, "time": 0.2, "clearance": 0.2, "smooth": 0.1, "no_fall": 0.1}
+METRICS = (*WEIGHTS, "collision_rate", "fall_rate", "arrival_rate", "ray_hit_rate")
+
+
+def evaluation_scenarios(custom):
+    scenarios = []
+    for seed in SEEDS:
+        value = validate_task_config(OBSTACLE_TASK, custom, seed)
+        scenarios.append(
+            {"seed": seed, "taskConfig": value, "terrain": build_terrain_layout(value)}
+        )
+    return scenarios
+
+
+def protocol(custom, num_envs):
+    return {
+        "protocolVersion": "obstacle-v1",
+        "seeds": list(SEEDS),
+        "stepsPerSeed": STEPS,
+        "numEnvs": num_envs,
+        "objectiveWeights": WEIGHTS,
+        "sceneMode": "fixed-custom-map"
+        if custom["terrainPreset"] == "custom_boxes"
+        else "three-seed-layouts",
+        "scenarios": evaluation_scenarios(custom),
+        "episodePolicy": "first-episode-only; terminal snapshot before auto-reset; fixed horizon",
+    }
+
+
+def score_trajectory(samples, horizon=STEPS):
+    """One first-episode trajectory, one sample per policy step including first terminal."""
+    if not samples or len(samples) > horizon:
+        raise EvaluationError("轨迹样本数不完整")
+    keys = {"distance", "clearance", "action_delta", "ray_hit", "collision", "fall", "terminal"}
+    for sample in samples:
+        if set(sample) != keys:
+            raise EvaluationError("轨迹指标缺失/未知")
+        for key in keys:
+            value = sample[key]
+            if not isinstance(value, (int, float)) or not math.isfinite(value) or value < 0:
+                raise EvaluationError(f"无效轨迹指标 {key}")
+            if key in {"ray_hit", "collision", "fall", "terminal"} and value > 1:
+                raise EvaluationError(f"无效标志 {key}")
+    if any(s["terminal"] for s in samples[:-1]) or (
+        len(samples) != horizon and not samples[-1]["terminal"]
+    ):
+        raise EvaluationError("首episode轨迹不完整")
+    collision = any(s["collision"] for s in samples)
+    fall = any(s["fall"] for s in samples)
+    arrival = next((i + 1 for i, s in enumerate(samples) if s["distance"] < 0.5), None)
+    success = arrival is not None and not collision and not fall
+    # Missing steps after an early terminal earn zero clearance/smoothness, not a bonus.
+    return {
+        "success": float(success),
+        "time": 1 - arrival / horizon if success else 0.0,
+        "clearance": 0.0 if fall else sum(min(s["clearance"] / 0.5, 1) for s in samples) / horizon,
+        "smooth": sum(1 - min(s["action_delta"], 1) for s in samples) / horizon,
+        "no_fall": float(not fall),
+        "collision_rate": float(collision),
+        "fall_rate": float(fall),
+        "arrival_rate": float(arrival is not None),
+        "ray_hit_rate": fmean(s["ray_hit"] for s in samples),
+    }
+
+
+def validate_metrics(value):
+    if not isinstance(value, dict) or set(value) != set(METRICS):
+        raise EvaluationError("避障评估指标缺失/未知")
+    for key, number in value.items():
+        if (
+            isinstance(number, bool)
+            or not isinstance(number, (int, float))
+            or not math.isfinite(number)
+            or not 0 <= number <= 1
+        ):
+            raise EvaluationError(f"避障指标 {key} 必须在0–1且有限")
+    if not math.isclose(value["no_fall"] + value["fall_rate"], 1, abs_tol=1e-8):
+        raise EvaluationError("跌倒指标不一致")
+    return value
+
+
+def validate_evaluation(value, expected_protocol):
+    if value.get("protocol") != expected_protocol:
+        raise EvaluationError("避障评估协议/场景不一致")
+    seeds = value.get("seedMetrics", [])
+    if len(seeds) != 3:
+        raise EvaluationError("避障评估必须完整包含3seed")
+    for item, seed in zip(seeds, SEEDS, strict=True):
+        if (
+            item.get("seed") != seed
+            or item.get("episodes") != expected_protocol["numEnvs"]
+            or item.get("rolloutSteps") != STEPS
+        ):
+            raise EvaluationError("避障评估样本数不完整")
+        validate_metrics(item.get("metrics"))
+    metrics = validate_metrics(value.get("metrics"))
+    if any(
+        not math.isclose(metrics[k], fmean(s["metrics"][k] for s in seeds), abs_tol=1e-8)
+        for k in METRICS
+    ):
+        raise EvaluationError("避障评估聚合不一致")
+    return metrics
+
+
+def score_evaluation(current, baseline=None):
+    current = validate_metrics(current)
+    baseline = validate_metrics(baseline) if baseline is not None else current
+    eligible = (
+        current["fall_rate"] <= baseline["fall_rate"] + 0.02 + 1e-12
+        and current["success"] >= baseline["success"] - 0.02 - 1e-12
+    )
+    total = sum(WEIGHTS[k] * current[k] for k in WEIGHTS)
+    return {
+        "eligible": eligible,
+        "score": total if eligible else -1.0,
+        "components": {k: current[k] for k in WEIGHTS},
+        "metrics": current,
+    }