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

training_server/tuning/__init__.py

@@ -0,0 +1,26 @@
+"""Reward auto-tuning support for the local training service."""
+
+from .schema import (
+    BASE_REWARD_CONFIGURATION,
+    PARAMETER_SPECS,
+    WEIGHT_SPECS,
+    RewardConfigError,
+    apply_reward_configuration,
+    merge_proposal,
+    validate_configuration,
+    validate_proposal,
+)
+from .scoring import DEFAULT_OBJECTIVE_WEIGHTS, score_evaluation
+
+__all__ = [
+    "BASE_REWARD_CONFIGURATION",
+    "DEFAULT_OBJECTIVE_WEIGHTS",
+    "PARAMETER_SPECS",
+    "WEIGHT_SPECS",
+    "RewardConfigError",
+    "apply_reward_configuration",
+    "merge_proposal",
+    "score_evaluation",
+    "validate_configuration",
+    "validate_proposal",
+]

training_server/tuning/advisor.py

@@ -0,0 +1,134 @@
+"""PydanticAI adapter for the DeepSeek reward-tuning advisor."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+from dataclasses import dataclass
+from typing import Any
+
+from .schema import validate_proposal
+
+SYSTEM_PROMPT = """你是 Unitree Go2 强化学习奖励调参专家。
+只根据提供的数值配置、训练曲线摘要和固定评估结果提出下一轮稀疏修改。
+必须优先保持速度跟踪与跌倒安全门槛；每轮最多修改四个白名单标量，不得改变符号、函数、传感器或结构。
+不要建议 Python 代码、命令、文件路径或白名单外参数。输出必须符合 RewardProposal schema。
+"""
+
+
+class AdvisorUnavailable(RuntimeError):
+    pass
+
+
+@dataclass(frozen=True)
+class AdvisorConfig:
+    api_key: str | None
+    base_url: str = "https://api.deepseek.com"
+    model: str = "deepseek-v4-flash"
+
+    @classmethod
+    def from_environment(cls) -> AdvisorConfig:
+        return cls(
+            api_key=os.environ.get("DEEPSEEK_API_KEY"),
+            base_url=os.environ.get("MUJOCO_TUNING_AGENT_BASE_URL", "https://api.deepseek.com"),
+            model=os.environ.get("MUJOCO_TUNING_AGENT_MODEL", "deepseek-v4-flash"),
+        )
+
+
+class DeepSeekAdvisor:
+    def __init__(self, config: AdvisorConfig | None = None):
+        self.config = config or AdvisorConfig.from_environment()
+        self._cached_agent = None
+
+    def capability(self) -> dict[str, Any]:
+        try:
+            import pydantic_ai  # noqa: F401
+        except ImportError:
+            installed = False
+        else:
+            installed = True
+        return {
+            "configured": bool(self.config.api_key) and installed,
+            "apiKeyConfigured": bool(self.config.api_key),
+            "frameworkInstalled": installed,
+            "model": self.config.model,
+            "baseUrl": self.config.base_url,
+        }
+
+    def _agent(self):
+        if self._cached_agent is not None:
+            return self._cached_agent
+        if not self.config.api_key:
+            raise AdvisorUnavailable("未配置 DEEPSEEK_API_KEY")
+        try:
+            import httpx2
+            from pydantic import BaseModel, Field
+            from pydantic_ai import Agent, PromptedOutput
+            from pydantic_ai.models.openai import OpenAIChatModel
+            from pydantic_ai.providers.openai import OpenAIProvider
+        except ImportError as error:
+            raise AdvisorUnavailable(
+                "缺少 PydanticAI，请安装 training_server/requirements.txt"
+            ) from error
+
+        class RewardProposalOutput(BaseModel):
+            weights: dict[str, float] = Field(default_factory=dict)
+            params: dict[str, float] = Field(default_factory=dict)
+            rationale: str = Field(min_length=1, max_length=2000)
+            expected_impact: dict[str, str] = Field(default_factory=dict)
+            confidence: float = Field(ge=0.0, le=1.0)
+
+        proxy = os.environ.get("HTTPS_PROXY") or os.environ.get("ALL_PROXY")
+        if proxy and proxy.startswith("socks://"):
+            proxy = "socks5://" + proxy.removeprefix("socks://")
+        http_client = httpx2.AsyncClient(proxy=proxy, trust_env=False, timeout=60.0)
+        provider = OpenAIProvider(
+            base_url=self.config.base_url, api_key=self.config.api_key, http_client=http_client
+        )
+        model = OpenAIChatModel(self.config.model, provider=provider)  # type: ignore[arg-type]
+        self._cached_agent = Agent(
+            model,
+            output_type=PromptedOutput(RewardProposalOutput),
+            system_prompt=SYSTEM_PROMPT,
+            retries=2,
+            model_settings={"temperature": 0.2},
+        )
+        return self._cached_agent
+
+    def propose(self, context: dict[str, Any], previous: dict) -> dict[str, Any]:
+        prompt = json.dumps(context, ensure_ascii=False, separators=(",", ":"), allow_nan=False)
+        result = self._agent().run_sync(prompt)
+        output = result.output
+        patch = validate_proposal(
+            {"weights": dict(output.weights), "params": dict(output.params)}, previous
+        )
+        try:
+            usage = result.usage()
+            usage_value = {
+                key: getattr(usage, key)
+                for key in ("requests", "input_tokens", "output_tokens", "total_tokens")
+                if getattr(usage, key, None) is not None
+            }
+        except (AttributeError, TypeError):
+            usage_value = {}
+        return {
+            "patch": patch,
+            "rationale": output.rationale,
+            "expectedImpact": dict(output.expected_impact),
+            "confidence": float(output.confidence),
+            "promptHash": hashlib.sha256(prompt.encode()).hexdigest(),
+            "usage": usage_value,
+            "model": self.config.model,
+        }
+
+    def test_connection(self) -> dict[str, Any]:
+        base = {
+            "weights": {"track_linear_velocity": 1.0},
+            "params": {},
+            "instruction": "仅返回一个合法示例：把 track_linear_velocity 改为 1.1。",
+        }
+        # A minimal full previous config is supplied by callers for actual proposals;
+        # connectivity probing only verifies the provider and structured response path.
+        result = self._agent().run_sync(json.dumps(base, ensure_ascii=False))
+        return {"ok": True, "model": self.config.model, "outputType": type(result.output).__name__}

training_server/tuning/manager.py

@@ -0,0 +1,715 @@
+"""Persistent reward tuning session orchestrator."""
+
+from __future__ import annotations
+
+import json
+import os
+import re
+import shutil
+import subprocess
+import threading
+from contextlib import suppress
+from copy import deepcopy
+from pathlib import Path
+from typing import Any
+
+from .advisor import AdvisorUnavailable, DeepSeekAdvisor
+from .process import GpuLease, ResourceBusyError, terminate_process
+from .schema import (
+    BASE_REWARD_CONFIGURATION,
+    merge_proposal,
+    validate_proposal,
+)
+from .scoring import DEFAULT_OBJECTIVE_WEIGHTS, score_evaluation, validate_objective_weights
+from .storage import TuningStorage, now_iso
+from .study import OptunaStudies
+from .tensorboard import ingest_scalars
+
+ACTIVE_SESSION_STATES = {
+    "queued",
+    "running",
+    "evaluating",
+    "awaiting_approval",
+    "paused",
+    "interrupted",
+}
+RUNNING_STATES = {"queued", "running", "evaluating"}
+RUN_NAME = re.compile(r"^[A-Za-z0-9_.-]{1,64}$")
+
+
+class TuningError(RuntimeError):
+    pass
+
+
+class TuningManager:
+    def __init__(
+        self,
+        trainer_root: Path,
+        python: str,
+        data_root: Path,
+        lease: GpuLease,
+        advisor: Any | None = None,
+    ):
+        self.trainer_root = trainer_root.expanduser().resolve()
+        self.python = python
+        self.data_root = data_root.expanduser().resolve()
+        self.data_root.mkdir(parents=True, exist_ok=True)
+        self.storage = TuningStorage(self.data_root / "tuning.sqlite3")
+        self.storage.recover_interrupted()
+        self.studies = OptunaStudies(self.data_root)
+        self.lease = lease
+        self.advisor = advisor or DeepSeekAdvisor()
+        self.lock = threading.RLock()
+        self.condition = threading.Condition(self.lock)
+        self.workers: dict[str, threading.Thread] = {}
+        self.processes: dict[str, subprocess.Popen[str]] = {}
+        self.cancel_events: dict[str, threading.Event] = {}
+
+    def capability(self) -> dict[str, Any]:
+        capability = self.advisor.capability()
+        capability.update({"ready": (self.trainer_root / "scripts" / "evaluate.py").is_file()})
+        return capability
+
+    @staticmethod
+    def _integer(payload: dict, name: str, default: int, minimum: int, maximum: int) -> int:
+        value = payload.get(name, default)
+        if isinstance(value, bool) or not isinstance(value, int) or not minimum <= value <= maximum:
+            raise TuningError(f"{name} 必须在 {minimum}–{maximum} 之间")
+        return value
+
+    def parse_create(self, payload: Any) -> tuple[str, dict, dict, bool]:
+        if not isinstance(payload, dict):
+            raise TuningError("请求体必须是 JSON 对象")
+        mode = payload.get("mode", "automatic")
+        if mode not in ("automatic", "approval"):
+            raise TuningError("mode 必须是 automatic 或 approval")
+        if payload.get("taskId", "Unitree-Go2-Flat") != "Unitree-Go2-Flat":
+            raise TuningError("第一版只支持 Unitree-Go2-Flat")
+        run_name = payload.get("runName", "auto-tune")
+        if not isinstance(run_name, str) or not RUN_NAME.fullmatch(run_name):
+            raise TuningError("runName 格式无效")
+        gpu_ids = payload.get("gpuIds", [0])
+        if (
+            not isinstance(gpu_ids, list)
+            or not gpu_ids
+            or any(
+                isinstance(value, bool) or not isinstance(value, int) or not 0 <= value <= 255
+                for value in gpu_ids
+            )
+        ):
+            raise TuningError("gpuIds 必须是非空非负整数数组")
+        trial_count = self._integer(payload, "trialCount", 12, 4, 20)
+        rung0 = self._integer(payload, "initialIterations", 300, 1, 1000000)
+        rung1 = self._integer(payload, "middleIterations", 900, rung0, 1000000)
+        rung2 = self._integer(payload, "finalIterations", 2000, rung1, 1000000)
+        config = {
+            "taskId": "Unitree-Go2-Flat",
+            "numEnvs": self._integer(payload, "numEnvs", 4096, 1, 16384),
+            "seed": self._integer(payload, "seed", 42, 0, 2147483647),
+            "runName": run_name,
+            "gpuIds": gpu_ids,
+            "trialCount": trial_count,
+            "rungs": [rung0, rung1, rung2],
+            "promote": [trial_count, min(4, trial_count), min(2, trial_count)],
+            "evalNumEnvs": self._integer(payload, "evalNumEnvs", 256, 1, 4096),
+            "evalSteps": self._integer(payload, "evalSteps", 1000, 10, 100000),
+            "earlyStopPatience": self._integer(payload, "earlyStopPatience", 4, 1, 20),
+        }
+        objective = validate_objective_weights(
+            payload.get("objectiveWeights", DEFAULT_OBJECTIVE_WEIGHTS)
+        )
+        fallback = payload.get("fallbackEnabled", False)
+        if not isinstance(fallback, bool):
+            raise TuningError("fallbackEnabled 必须是布尔值")
+        return mode, config, objective, fallback
+
+    def create(self, payload: Any) -> dict:
+        mode, config, objective, fallback = self.parse_create(payload)
+        capability = self.capability()
+        if not capability["ready"]:
+            raise TuningError("评估入口未就绪")
+        if not capability["configured"] and not fallback:
+            raise TuningError("DeepSeek Agent 未配置；设置 DEEPSEEK_API_KEY 或显式启用 fallback")
+        with self.lock:
+            active = [
+                session
+                for session in self.storage.list_sessions()
+                if session["state"] in ACTIVE_SESSION_STATES
+            ]
+            if active:
+                raise ResourceBusyError("已有调参 session 未结束")
+            session = self.storage.create_session(mode, config, objective, fallback)
+            self._start_worker(session["id"], resume=False)
+            return self.detail(session["id"])
+
+    def _start_worker(self, session_id: str, resume: bool) -> None:
+        cancel = threading.Event()
+        self.cancel_events[session_id] = cancel
+        worker = threading.Thread(
+            target=self._run_session,
+            args=(session_id, resume, cancel),
+            name=f"tuning-{session_id[:8]}",
+            daemon=True,
+        )
+        self.workers[session_id] = worker
+        worker.start()
+
+    def detail(self, session_id: str) -> dict:
+        session = self.storage.get_session(session_id)
+        session["trials"] = self.storage.list_trials(session_id)
+        session["proposals"] = self.storage.list_proposals(session_id)
+        session["audit"] = self.storage.audit_events(session_id)
+        return session
+
+    def list(self) -> list[dict]:
+        return self.storage.list_sessions()
+
+    def _session_root(self, session_id: str) -> Path:
+        root = (self.data_root / "sessions" / session_id).resolve()
+        if not root.is_relative_to(self.data_root):
+            raise TuningError("非法 session 路径")
+        root.mkdir(parents=True, exist_ok=True)
+        return root
+
+    def _run_command(
+        self,
+        session_id: str,
+        command: list[str],
+        cwd: Path,
+        environment: dict[str, str],
+        log_path: Path,
+    ) -> int:
+        owner = f"tuning:{session_id}"
+        self.lease.acquire(owner)
+        try:
+            process = subprocess.Popen(
+                command,
+                cwd=cwd,
+                env=environment,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.STDOUT,
+                text=True,
+                encoding="utf-8",
+                errors="replace",
+                bufsize=1,
+                start_new_session=True,
+            )
+            with self.lock:
+                self.processes[session_id] = process
+            assert process.stdout is not None
+            with log_path.open("a", encoding="utf-8") as log:
+                for line in process.stdout:
+                    log.write(line)
+                    log.flush()
+                    if self.cancel_events[session_id].is_set():
+                        terminate_process(process)
+                        break
+            return process.wait()
+        finally:
+            with self.lock:
+                self.processes.pop(session_id, None)
+            self.lease.release(owner)
+
+    @staticmethod
+    def _latest_checkpoint(run_dir: Path) -> Path | None:
+        values = []
+        for path in run_dir.glob("model_*.pt"):
+            match = re.fullmatch(r"model_(\d+)\.pt", path.name)
+            if match:
+                values.append((int(match.group(1)), path))
+        return max(values, default=(0, None), key=lambda value: value[0])[1]
+
+    def _execute_trial(
+        self, session: dict, trial: dict, resume_checkpoint: Path | None = None
+    ) -> dict:
+        session_id, trial_id = session["id"], trial["id"]
+        root = self._session_root(session_id)
+        run_dir = (root / trial["runDir"]).resolve()
+        if not run_dir.is_relative_to(root):
+            raise TuningError("trial 目录越界")
+        run_dir.mkdir(parents=True, exist_ok=True)
+        reward_path = run_dir / "reward_config.json"
+        reward_path.write_text(
+            json.dumps(trial["rewardConfig"], ensure_ascii=False, indent=2) + "\n", encoding="utf-8"
+        )
+        config = session["config"]
+        command = [
+            self.python,
+            "-u",
+            "scripts/train.py",
+            config["taskId"],
+            f"--env.scene.num-envs={config['numEnvs']}",
+            f"--agent.max-iterations={trial['targetIterations']}",
+            f"--agent.seed={config['seed']}",
+            f"--agent.run-name={config['runName']}-t{trial['number']}-r{trial['rung']}",
+            "--agent.logger=tensorboard",
+            "--agent.upload-model=False",
+            "--gpu-ids",
+            json.dumps(config["gpuIds"], separators=(",", ":")),
+            "--output-dir",
+            str(run_dir),
+            "--reward-config",
+            str(reward_path),
+        ]
+        if resume_checkpoint is not None:
+            command.extend(("--resume-checkpoint", str(resume_checkpoint)))
+        environment = os.environ.copy()
+        environment["WANDB_MODE"] = "disabled"
+        environment["WANDB_SILENT"] = "true"
+        self.storage.update_trial(
+            trial_id, state="training", started_at=now_iso(), message="正在训练"
+        )
+        self.storage.update_session(
+            session_id,
+            state="running",
+            message=f"正在训练 trial {trial['number']} / rung {trial['rung']}",
+        )
+        return_code = self._run_command(
+            session_id, command, self.trainer_root, environment, run_dir / "train.log"
+        )
+        ingest_scalars(self.storage, trial_id, run_dir)
+        if self.cancel_events[session_id].is_set():
+            raise TuningError("session 已取消")
+        checkpoint = self._latest_checkpoint(run_dir)
+        policy = run_dir / "policy.onnx"
+        if return_code != 0 or checkpoint is None or not policy.is_file():
+            raise TuningError(f"训练失败（返回码 {return_code}）或缺少 checkpoint/policy.onnx")
+        self._wait_if_paused(session_id, self.cancel_events[session_id])
+
+        eval_output = run_dir / "evaluation.json"
+        eval_command = [
+            self.python,
+            "-u",
+            "scripts/evaluate.py",
+            config["taskId"],
+            "--checkpoint",
+            str(checkpoint),
+            "--output",
+            str(eval_output),
+            "--reward-config",
+            str(reward_path),
+            f"--num-envs={config['evalNumEnvs']}",
+            f"--steps-per-seed={config['evalSteps']}",
+            "--gpu-ids",
+            json.dumps(config["gpuIds"], separators=(",", ":")),
+        ]
+        self.storage.update_trial(trial_id, state="evaluating", message="正在固定协议评估")
+        self.storage.update_session(
+            session_id, state="evaluating", message=f"正在评估 trial {trial['number']}"
+        )
+        return_code = self._run_command(
+            session_id, eval_command, self.trainer_root, environment, run_dir / "evaluate.log"
+        )
+        ingest_scalars(self.storage, trial_id, run_dir / "evaluation-events")
+        if return_code != 0 or not eval_output.is_file():
+            raise TuningError(f"评估失败（返回码 {return_code}）")
+        evaluation = json.loads(eval_output.read_text(encoding="utf-8"))
+        baseline_trial = self.storage.list_trials(session_id)[0]
+        if baseline_trial["evaluation"] is None:
+            scored = {
+                "eligible": True,
+                "score": 0.0,
+                "components": {},
+                "metrics": evaluation["metrics"],
+            }
+        else:
+            scored = score_evaluation(
+                baseline_trial["evaluation"]["metrics"],
+                evaluation["metrics"],
+                session["objectiveWeights"],
+            )
+        evaluation["score"] = scored
+        rel_checkpoint = str(checkpoint.relative_to(root))
+        rel_policy = str(policy.relative_to(root))
+        self.storage.update_trial(
+            trial_id,
+            state="completed",
+            ended_at=now_iso(),
+            message="训练与评估完成",
+            checkpoint_path=rel_checkpoint,
+            policy_path=rel_policy,
+            evaluation=evaluation,
+            score=scored["score"],
+            eligible=scored["eligible"],
+        )
+        try:
+            optuna_number = self.studies.record(
+                session_id,
+                trial["rewardConfig"],
+                scored["score"],
+                scored["eligible"],
+                trial["rung"],
+            )
+            self.storage.audit(
+                session_id, "optuna_trial_recorded", {"trialId": trial_id, "number": optuna_number}
+            )
+        except Exception as error:
+            self.storage.audit(
+                session_id, "optuna_record_failed", {"trialId": trial_id, "error": str(error)[:500]}
+            )
+        return self.storage.get_trial(trial_id)
+
+    def _best(self, session_id: str, rung: int | None = None) -> dict | None:
+        trials = [
+            trial
+            for trial in self.storage.list_trials(session_id)
+            if trial["state"] == "completed" and trial["eligible"]
+        ]
+        if rung is not None:
+            trials = [trial for trial in trials if trial["rung"] == rung]
+        return max(
+            trials,
+            key=lambda trial: trial["score"] if trial["score"] is not None else -999,
+            default=None,
+        )
+
+    def _proposal_context(self, session: dict) -> dict:
+        trials = self.storage.list_trials(session["id"])[-12:]
+        rejected_feedback = [
+            proposal["feedback"]
+            for proposal in self.storage.list_proposals(session["id"])
+            if proposal["state"] == "rejected" and proposal["feedback"]
+        ][-4:]
+        return {
+            "task": session["config"]["taskId"],
+            "objectiveWeights": session["objectiveWeights"],
+            "allowlist": "服务端将验证固定 schema；最多四项修改",
+            "rejectedFeedback": rejected_feedback,
+            "trials": [
+                {
+                    "number": t["number"],
+                    "rung": t["rung"],
+                    "score": t["score"],
+                    "eligible": t["eligible"],
+                    "rewardConfig": t["rewardConfig"],
+                    "evaluation": t["evaluation"] and t["evaluation"].get("metrics"),
+                }
+                for t in trials
+            ],
+        }
+
+    def _fallback_patch(self, previous: dict, index: int) -> dict:
+        names = ("track_linear_velocity", "action_rate_l2", "body_orientation_l2", "foot_slip")
+        name = names[index % len(names)]
+        old = previous["weights"][name]
+        factor = 1.1 if index % 2 == 0 else 0.9
+        return validate_proposal({"weights": {name: old * factor}}, previous)
+
+    def _request_proposal(
+        self, session: dict, previous: dict, base_trial_id: str, index: int
+    ) -> dict:
+        try:
+            result = self.advisor.propose(self._proposal_context(session), previous)
+            source = "agent"
+        except Exception as error:
+            if not session["fallbackEnabled"]:
+                raise AdvisorUnavailable(str(error)) from error
+            result = {
+                "patch": self._fallback_patch(previous, index),
+                "rationale": f"Agent 不可用，显式 fallback：{error}",
+                "expectedImpact": {},
+                "confidence": 0.2,
+                "promptHash": None,
+                "usage": {},
+                "model": "optuna-fallback",
+            }
+            source = "fallback"
+        proposal = self.storage.create_proposal(
+            session["id"],
+            base_trial_id,
+            result["patch"],
+            result["rationale"],
+            result.get("expectedImpact", {}),
+            result["confidence"],
+            source,
+        )
+        self.storage.audit(
+            session["id"],
+            "proposal_created",
+            {
+                "proposalId": proposal["id"],
+                "source": source,
+                "promptHash": result.get("promptHash"),
+                "usage": result.get("usage", {}),
+                "model": result.get("model"),
+            },
+        )
+        return proposal
+
+    def _wait_for_approval(
+        self, session_id: str, proposal_id: str, cancel: threading.Event
+    ) -> dict:
+        with self.condition:
+            while not cancel.is_set():
+                proposal = self.storage.get_proposal(proposal_id)
+                if proposal["state"] != "pending":
+                    return proposal
+                self.condition.wait(timeout=1.0)
+        raise TuningError("session 已取消")
+
+    def _run_session(self, session_id: str, resume: bool, cancel: threading.Event) -> None:
+        try:
+            session = self.storage.get_session(session_id)
+            trials = self.storage.list_trials(session_id)
+            if resume:
+                root = self._session_root(session_id)
+                for interrupted in [trial for trial in trials if trial["state"] == "interrupted"]:
+                    run_dir = (root / interrupted["runDir"]).resolve()
+                    if run_dir.is_relative_to(root):
+                        shutil.rmtree(run_dir, ignore_errors=True)
+                    self.storage.delete_trial(interrupted["id"])
+                self.storage.audit(
+                    session_id,
+                    "session_resumed",
+                    {
+                        "discardedInterruptedTrials": [
+                            t["id"] for t in trials if t["state"] == "interrupted"
+                        ]
+                    },
+                )
+                trials = self.storage.list_trials(session_id)
+            if not trials:
+                baseline_dir = "trial-000-rung-0"
+                trial = self.storage.create_trial(
+                    session_id,
+                    0,
+                    0,
+                    session["config"]["rungs"][0],
+                    deepcopy(BASE_REWARD_CONFIGURATION),
+                    None,
+                    baseline_dir,
+                )
+                self._execute_trial(session, trial)
+            session = self.storage.get_session(session_id)
+            completed_rung0 = [
+                t
+                for t in self.storage.list_trials(session_id)
+                if t["rung"] == 0 and t["state"] == "completed"
+            ]
+            next_number = len({t["number"] for t in completed_rung0})
+            best_score = max((t["score"] or 0.0 for t in completed_rung0), default=0.0)
+            no_improve = 0
+            while (
+                next_number < session["config"]["trialCount"]
+                and no_improve < session["config"]["earlyStopPatience"]
+            ):
+                if cancel.is_set():
+                    raise TuningError("session 已取消")
+                self._wait_if_paused(session_id, cancel)
+                base = self._best(session_id, rung=0) or completed_rung0[0]
+                proposal = self._request_proposal(
+                    session, base["rewardConfig"], base["id"], next_number
+                )
+                if session["mode"] == "approval":
+                    self.storage.update_session(
+                        session_id, state="awaiting_approval", message="等待批准 Agent 建议"
+                    )
+                    proposal = self._wait_for_approval(session_id, proposal["id"], cancel)
+                    if proposal["state"] == "rejected":
+                        self.storage.audit(
+                            session_id,
+                            "proposal_rejected",
+                            {"proposalId": proposal["id"], "feedback": proposal["feedback"]},
+                        )
+                        continue
+                else:
+                    self.storage.decide_proposal(proposal["id"], "approved", "自动模式")
+                    proposal = self.storage.get_proposal(proposal["id"])
+                self._wait_if_paused(session_id, cancel)
+                reward_config = merge_proposal(base["rewardConfig"], proposal["patch"])
+                trial = self.storage.create_trial(
+                    session_id,
+                    next_number,
+                    0,
+                    session["config"]["rungs"][0],
+                    reward_config,
+                    proposal["id"],
+                    f"trial-{next_number:03d}-rung-0",
+                )
+                result = self._execute_trial(session, trial)
+                if result["eligible"] and (result["score"] or -999) > best_score + 0.01:
+                    best_score = result["score"]
+                    no_improve = 0
+                else:
+                    no_improve += 1
+                self.storage.update_session(session_id, consecutive_no_improve=no_improve)
+                next_number += 1
+
+            # Promote top configurations; each new rung resumes its own previous checkpoint.
+            for rung in (1, 2):
+                self._wait_if_paused(session_id, cancel)
+                previous = [
+                    t
+                    for t in self.storage.list_trials(session_id)
+                    if t["rung"] == rung - 1 and t["state"] == "completed" and t["eligible"]
+                ]
+                previous.sort(key=lambda t: t["score"] or -999, reverse=True)
+                promoted_numbers = {
+                    trial["number"]
+                    for trial in self.storage.list_trials(session_id)
+                    if trial["rung"] == rung and trial["state"] == "completed"
+                }
+                for parent in previous[: session["config"]["promote"][rung]]:
+                    if parent["number"] in promoted_numbers:
+                        continue
+                    if cancel.is_set():
+                        raise TuningError("session 已取消")
+                    root = self._session_root(session_id)
+                    checkpoint = root / parent["checkpointPath"]
+                    trial = self.storage.create_trial(
+                        session_id,
+                        parent["number"],
+                        rung,
+                        session["config"]["rungs"][rung],
+                        parent["rewardConfig"],
+                        parent["proposalId"],
+                        f"trial-{parent['number']:03d}-rung-{rung}",
+                    )
+                    self._execute_trial(session, trial, checkpoint)
+
+            best = (
+                self._best(session_id, rung=2)
+                or self._best(session_id, rung=1)
+                or self._best(session_id, rung=0)
+            )
+            if best is None:
+                raise TuningError("没有通过安全门槛的 trial")
+            preset_name = f"{session['config']['runName']}-{session_id[:8]}"
+            self.storage.save_preset(preset_name, session_id, best["id"], best["rewardConfig"])
+            self.storage.update_session(
+                session_id,
+                state="succeeded",
+                best_trial_id=best["id"],
+                current_trial_id=None,
+                message="调参完成",
+            )
+            self.storage.audit(
+                session_id, "session_completed", {"bestTrialId": best["id"], "preset": preset_name}
+            )
+        except Exception as error:
+            state = self.storage.get_session(session_id)["state"]
+            if cancel.is_set() or state == "cancelled":
+                self.storage.update_session(
+                    session_id, state="cancelled", message="调参已取消", current_trial_id=None
+                )
+            else:
+                self.storage.update_session(
+                    session_id, state="failed", message=str(error), current_trial_id=None
+                )
+                self.storage.audit(session_id, "session_failed", {"error": str(error)[:1000]})
+        finally:
+            with self.lock:
+                self.workers.pop(session_id, None)
+                self.processes.pop(session_id, None)
+
+    def _wait_if_paused(self, session_id: str, cancel: threading.Event) -> None:
+        with self.condition:
+            while self.storage.get_session(session_id)["state"] == "paused" and not cancel.is_set():
+                self.condition.wait(timeout=1.0)
+
+    def approve(self, session_id: str, proposal_id: str, payload: Any) -> dict:
+        proposal = self.storage.get_proposal(proposal_id)
+        if proposal["sessionId"] != session_id:
+            raise TuningError("proposal 不属于该 session")
+        patch = proposal["patch"]
+        feedback = None
+        if isinstance(payload, dict):
+            feedback = payload.get("feedback")
+            if "patch" in payload:
+                base = self.storage.get_trial(proposal["baseTrialId"])
+                patch = validate_proposal(payload["patch"], base["rewardConfig"])
+        if not self.storage.decide_proposal(proposal_id, "approved", feedback, patch):
+            raise TuningError("proposal 已处理")
+        self.storage.audit(
+            session_id,
+            "proposal_approved",
+            {"proposalId": proposal_id, "modified": patch != proposal["patch"]},
+        )
+        with self.condition:
+            self.condition.notify_all()
+        return self.detail(session_id)
+
+    def reject(self, session_id: str, proposal_id: str, payload: Any) -> dict:
+        feedback = payload.get("feedback", "") if isinstance(payload, dict) else ""
+        if not isinstance(feedback, str) or len(feedback) > 2000:
+            raise TuningError("feedback 无效")
+        proposal = self.storage.get_proposal(proposal_id)
+        if proposal["sessionId"] != session_id:
+            raise TuningError("proposal 不属于该 session")
+        if not self.storage.decide_proposal(proposal_id, "rejected", feedback):
+            raise TuningError("proposal 已处理")
+        with self.condition:
+            self.condition.notify_all()
+        return self.detail(session_id)
+
+    def pause(self, session_id: str) -> dict:
+        session = self.storage.get_session(session_id)
+        if session["state"] not in RUNNING_STATES | {"awaiting_approval"}:
+            raise TuningError("当前状态不能暂停")
+        self.storage.update_session(
+            session_id, state="paused", message="已暂停后续调度；当前子进程将完成"
+        )
+        return self.detail(session_id)
+
+    def resume(self, session_id: str) -> dict:
+        session = self.storage.get_session(session_id)
+        if session["state"] == "paused":
+            self.storage.update_session(session_id, state="running", message="继续调参")
+            with self.condition:
+                self.condition.notify_all()
+        elif session["state"] == "interrupted":
+            self.storage.update_session(session_id, state="queued", message="从最近完整结果恢复")
+            self._start_worker(session_id, resume=True)
+        else:
+            raise TuningError("当前状态不能恢复")
+        return self.detail(session_id)
+
+    def cancel(self, session_id: str) -> dict:
+        self.storage.get_session(session_id)
+        self.storage.update_session(session_id, state="cancelled", message="正在取消")
+        event = self.cancel_events.get(session_id)
+        if event:
+            event.set()
+        process = self.processes.get(session_id)
+        if process:
+            terminate_process(process)
+        with self.condition:
+            self.condition.notify_all()
+        return self.detail(session_id)
+
+    def metrics(
+        self, session_id: str, trial_id: str, tags: list[str] | None, max_points: int
+    ) -> dict:
+        trial = self.storage.get_trial(trial_id)
+        if trial["sessionId"] != session_id:
+            raise TuningError("trial 不属于该 session")
+        return {"trialId": trial_id, "series": self.storage.metrics(trial_id, tags, max_points)}
+
+    def best_artifact(self, session_id: str) -> Path:
+        session = self.storage.get_session(session_id)
+        if not session["bestTrialId"]:
+            raise TuningError("尚无最佳策略")
+        trial = self.storage.get_trial(session["bestTrialId"])
+        if not trial["policyPath"]:
+            raise TuningError("最佳策略文件不存在")
+        root = self._session_root(session_id)
+        path = (root / trial["policyPath"]).resolve()
+        if not path.is_relative_to(root) or not path.is_file():
+            raise TuningError("最佳策略文件不存在")
+        return path
+
+    def preset_config(self, preset_id: str) -> dict:
+        return self.storage.get_preset(preset_id)["rewardConfig"]
+
+    def test_agent(self) -> dict:
+        try:
+            return self.advisor.test_connection()
+        except Exception as error:
+            raise TuningError(f"Agent 连接测试失败：{error}") from error
+
+    def shutdown(self) -> None:
+        for session_id in list(self.workers):
+            with suppress(KeyError, TuningError):
+                self.cancel(session_id)
+        for worker in list(self.workers.values()):
+            worker.join(timeout=7)

training_server/tuning/process.py

@@ -0,0 +1,46 @@
+"""Shared GPU lease and process-group lifecycle helpers."""
+
+from __future__ import annotations
+
+import os
+import signal
+import subprocess
+import threading
+from contextlib import suppress
+
+
+class ResourceBusyError(RuntimeError):
+    pass
+
+
+class GpuLease:
+    def __init__(self):
+        self.lock = threading.RLock()
+        self.owner: str | None = None
+
+    def acquire(self, owner: str) -> None:
+        with self.lock:
+            if self.owner is not None and self.owner != owner:
+                raise ResourceBusyError(f"计算资源正由 {self.owner} 使用")
+            self.owner = owner
+
+    def release(self, owner: str) -> None:
+        with self.lock:
+            if self.owner == owner:
+                self.owner = None
+
+    def public(self) -> str | None:
+        with self.lock:
+            return self.owner
+
+
+def terminate_process(process: subprocess.Popen[str], grace_seconds: float = 5.0) -> None:
+    if process.poll() is not None:
+        return
+    with suppress(ProcessLookupError):
+        os.killpg(process.pid, signal.SIGTERM)
+    try:
+        process.wait(timeout=grace_seconds)
+    except subprocess.TimeoutExpired:
+        with suppress(ProcessLookupError):
+            os.killpg(process.pid, signal.SIGKILL)

training_server/tuning/schema.py

@@ -0,0 +1,213 @@
+"""Pure-Python reward tuning schema shared by the service and trainer."""
+
+from __future__ import annotations
+
+import math
+from collections.abc import Mapping
+from copy import deepcopy
+from dataclasses import dataclass
+from typing import Any
+
+MAX_PROPOSAL_CHANGES = 4
+MIN_CHANGE_RATIO = 0.5
+MAX_CHANGE_RATIO = 2.0
+
+
+class RewardConfigError(ValueError):
+    """A reward configuration or proposal violated the allowlist."""
+
+
+@dataclass(frozen=True)
+class NumericSpec:
+    minimum: float
+    maximum: float
+    default: float
+    allow_zero: bool = True
+
+
+WEIGHT_SPECS: dict[str, NumericSpec] = {
+    "track_linear_velocity": NumericSpec(0.5, 3.0, 1.0, False),
+    "track_angular_velocity": NumericSpec(0.25, 2.0, 1.0, False),
+    "body_orientation_l2": NumericSpec(-3.0, -0.1, -1.0, False),
+    "pose": NumericSpec(0.0, 2.5, 1.0),
+    "body_ang_vel": NumericSpec(-0.2, 0.0, -0.05),
+    "angular_momentum": NumericSpec(-0.1, 0.0, -0.025),
+    "is_terminated": NumericSpec(-400.0, -50.0, -200.0, False),
+    "joint_acc_l2": NumericSpec(-2.0e-6, 0.0, -2.5e-7),
+    "joint_pos_limits": NumericSpec(-30.0, -2.0, -10.0, False),
+    "action_rate_l2": NumericSpec(-0.2, -0.005, -0.05, False),
+    "foot_gait": NumericSpec(0.0, 1.5, 0.5),
+    "foot_clearance": NumericSpec(-3.0, 0.0, -1.0),
+    "foot_slip": NumericSpec(-1.0, 0.0, -0.25),
+    "soft_landing": NumericSpec(-5.0e-3, 0.0, -1.0e-3),
+    "stand_still": NumericSpec(-3.0, 0.0, -1.0),
+    "electrical_power": NumericSpec(-5.0e-3, 0.0, 0.0),
+}
+
+PARAMETER_SPECS: dict[str, NumericSpec] = {
+    "track_linear_velocity.std": NumericSpec(0.25, 1.0, math.sqrt(0.25), False),
+    "track_angular_velocity.std": NumericSpec(0.35, 1.2, math.sqrt(0.5), False),
+    "pose.std_standing_scale": NumericSpec(0.5, 2.0, 1.0, False),
+    "pose.std_walking_scale": NumericSpec(0.5, 2.0, 1.0, False),
+    "pose.std_running_scale": NumericSpec(0.5, 2.0, 1.0, False),
+    "pose.walking_threshold": NumericSpec(0.05, 0.5, 0.1, False),
+    "pose.running_threshold": NumericSpec(1.0, 2.5, 1.5, False),
+    "foot_gait.period": NumericSpec(0.4, 0.8, 0.6, False),
+    "foot_gait.threshold": NumericSpec(0.45, 0.65, 0.56, False),
+    "foot_gait.command_threshold": NumericSpec(0.02, 0.3, 0.1, False),
+    "foot_clearance.target_height": NumericSpec(0.06, 0.16, 0.1, False),
+    "foot_clearance.command_threshold": NumericSpec(0.02, 0.3, 0.1, False),
+    "foot_slip.command_threshold": NumericSpec(0.02, 0.3, 0.1, False),
+    "soft_landing.command_threshold": NumericSpec(0.02, 0.3, 0.1, False),
+    "stand_still.command_threshold": NumericSpec(0.02, 0.3, 0.1, False),
+}
+
+BASE_REWARD_CONFIGURATION: dict[str, dict[str, float]] = {
+    "weights": {name: spec.default for name, spec in WEIGHT_SPECS.items()},
+    "params": {name: spec.default for name, spec in PARAMETER_SPECS.items()},
+}
+
+
+def _number(name: str, value: Any, spec: NumericSpec) -> float:
+    if isinstance(value, bool) or not isinstance(value, (int, float)):
+        raise RewardConfigError(f"{name} 必须是数值")
+    result = float(value)
+    if not math.isfinite(result):
+        raise RewardConfigError(f"{name} 必须是有限数值")
+    if result == 0.0 and not spec.allow_zero:
+        raise RewardConfigError(f"{name} 不允许关闭")
+    if result < spec.minimum or result > spec.maximum:
+        raise RewardConfigError(f"{name} 必须在 {spec.minimum}–{spec.maximum} 之间")
+    return result
+
+
+def _mapping(value: Any, name: str) -> Mapping[str, Any]:
+    if not isinstance(value, Mapping):
+        raise RewardConfigError(f"{name} 必须是对象")
+    return value
+
+
+def _cross_validate(config: Mapping[str, Mapping[str, float]]) -> None:
+    params = config["params"]
+    if params["pose.walking_threshold"] >= params["pose.running_threshold"]:
+        raise RewardConfigError("pose.walking_threshold 必须小于 pose.running_threshold")
+
+
+def validate_configuration(value: Any) -> dict[str, dict[str, float]]:
+    """Validate a complete configuration and reject missing/unknown fields."""
+    root = _mapping(value, "rewardConfig")
+    if set(root) != {"weights", "params"}:
+        raise RewardConfigError("rewardConfig 只能包含 weights 和 params")
+    raw_weights = _mapping(root["weights"], "weights")
+    raw_params = _mapping(root["params"], "params")
+    if set(raw_weights) != set(WEIGHT_SPECS):
+        raise RewardConfigError("weights 必须完整且不能包含未知奖励项")
+    if set(raw_params) != set(PARAMETER_SPECS):
+        raise RewardConfigError("params 必须完整且不能包含未知参数")
+    config = {
+        "weights": {
+            name: _number(f"weights.{name}", raw_weights[name], spec)
+            for name, spec in WEIGHT_SPECS.items()
+        },
+        "params": {
+            name: _number(f"params.{name}", raw_params[name], spec)
+            for name, spec in PARAMETER_SPECS.items()
+        },
+    }
+    _cross_validate(config)
+    return config
+
+
+def validate_proposal(value: Any, previous: Any) -> dict[str, dict[str, float]]:
+    """Validate a sparse Agent patch relative to a complete previous config."""
+    current = validate_configuration(previous)
+    root = _mapping(value, "proposal")
+    if not set(root).issubset({"weights", "params"}):
+        raise RewardConfigError("proposal 只能包含 weights 和 params")
+    raw_weights = _mapping(root.get("weights", {}), "weights")
+    raw_params = _mapping(root.get("params", {}), "params")
+    if len(raw_weights) + len(raw_params) == 0:
+        raise RewardConfigError("proposal 至少需要一项修改")
+    if len(raw_weights) + len(raw_params) > MAX_PROPOSAL_CHANGES:
+        raise RewardConfigError(f"proposal 每轮最多修改 {MAX_PROPOSAL_CHANGES} 项")
+    unknown_weights = set(raw_weights) - set(WEIGHT_SPECS)
+    unknown_params = set(raw_params) - set(PARAMETER_SPECS)
+    if unknown_weights:
+        raise RewardConfigError(f"未知奖励项：{', '.join(sorted(unknown_weights))}")
+    if unknown_params:
+        raise RewardConfigError(f"未知奖励参数：{', '.join(sorted(unknown_params))}")
+
+    patch: dict[str, dict[str, float]] = {"weights": {}, "params": {}}
+    for name, raw in raw_weights.items():
+        value_number = _number(f"weights.{name}", raw, WEIGHT_SPECS[name])
+        old = current["weights"][name]
+        if old != 0.0 and value_number != 0.0:
+            ratio = abs(value_number / old)
+            if ratio < MIN_CHANGE_RATIO or ratio > MAX_CHANGE_RATIO:
+                raise RewardConfigError(
+                    f"weights.{name} 单轮变化必须在旧值幅度的 "
+                    f"{MIN_CHANGE_RATIO}×–{MAX_CHANGE_RATIO}×"
+                )
+        if value_number == old:
+            raise RewardConfigError(f"weights.{name} 没有发生变化")
+        patch["weights"][name] = value_number
+    for name, raw in raw_params.items():
+        value_number = _number(f"params.{name}", raw, PARAMETER_SPECS[name])
+        old = current["params"][name]
+        ratio = abs(value_number / old)
+        if ratio < MIN_CHANGE_RATIO or ratio > MAX_CHANGE_RATIO:
+            raise RewardConfigError(
+                f"params.{name} 单轮变化必须在旧值的 {MIN_CHANGE_RATIO}×–{MAX_CHANGE_RATIO}×"
+            )
+        if value_number == old:
+            raise RewardConfigError(f"params.{name} 没有发生变化")
+        patch["params"][name] = value_number
+
+    candidate = deepcopy(current)
+    candidate["weights"].update(patch["weights"])
+    candidate["params"].update(patch["params"])
+    _cross_validate(candidate)
+    return patch
+
+
+def merge_proposal(previous: Any, proposal: Any) -> dict[str, dict[str, float]]:
+    current = validate_configuration(previous)
+    patch = validate_proposal(proposal, current)
+    merged = deepcopy(current)
+    merged["weights"].update(patch["weights"])
+    merged["params"].update(patch["params"])
+    return validate_configuration(merged)
+
+
+def apply_reward_configuration(env_cfg: Any, value: Any) -> None:
+    """Apply a validated full config to a fresh mjlab environment config."""
+    config = validate_configuration(value)
+    for name, weight in config["weights"].items():
+        if name not in env_cfg.rewards:
+            raise RewardConfigError(f"环境缺少奖励项：{name}")
+        env_cfg.rewards[name].weight = weight
+
+    params = config["params"]
+    direct = {
+        "track_linear_velocity.std": ("track_linear_velocity", "std"),
+        "track_angular_velocity.std": ("track_angular_velocity", "std"),
+        "pose.walking_threshold": ("pose", "walking_threshold"),
+        "pose.running_threshold": ("pose", "running_threshold"),
+        "foot_gait.period": ("foot_gait", "period"),
+        "foot_gait.threshold": ("foot_gait", "threshold"),
+        "foot_gait.command_threshold": ("foot_gait", "command_threshold"),
+        "foot_clearance.target_height": ("foot_clearance", "target_height"),
+        "foot_clearance.command_threshold": ("foot_clearance", "command_threshold"),
+        "foot_slip.command_threshold": ("foot_slip", "command_threshold"),
+        "soft_landing.command_threshold": ("soft_landing", "command_threshold"),
+        "stand_still.command_threshold": ("stand_still", "command_threshold"),
+    }
+    for path, (term, parameter) in direct.items():
+        env_cfg.rewards[term].params[parameter] = params[path]
+    for regime in ("standing", "walking", "running"):
+        key = f"std_{regime}"
+        scale = params[f"pose.{key}_scale"]
+        baseline = env_cfg.rewards["pose"].params[key]
+        env_cfg.rewards["pose"].params[key] = {
+            pattern: float(std) * scale for pattern, std in baseline.items()
+        }

training_server/tuning/scoring.py

@@ -0,0 +1,118 @@
+"""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,
+    }

training_server/tuning/storage.py

@@ -0,0 +1,521 @@
+"""SQLite persistence for tuning sessions, trials, proposals and scalar data."""
+
+from __future__ import annotations
+
+import json
+import sqlite3
+import threading
+import uuid
+from collections.abc import Iterator
+from contextlib import contextmanager
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Any
+
+SCHEMA_VERSION = 1
+
+
+def now_iso() -> str:
+    return datetime.now(UTC).isoformat()
+
+
+def _json(value: Any) -> str:
+    return json.dumps(value, ensure_ascii=False, separators=(",", ":"), allow_nan=False)
+
+
+def _decode(value: str | None) -> Any:
+    return json.loads(value) if value else None
+
+
+def _lttb(points: list[dict], threshold: int) -> list[dict]:
+    """Largest-Triangle-Three-Buckets downsampling preserving peaks and endpoints."""
+    if threshold >= len(points) or threshold < 3:
+        return points[:threshold]
+    sampled = [points[0]]
+    bucket_width = (len(points) - 2) / (threshold - 2)
+    anchor_index = 0
+    for bucket in range(threshold - 2):
+        average_start = int((bucket + 1) * bucket_width) + 1
+        average_end = min(int((bucket + 2) * bucket_width) + 1, len(points))
+        average_bucket = points[average_start:average_end] or [points[-1]]
+        average_x = sum(point["step"] for point in average_bucket) / len(average_bucket)
+        average_y = sum(point["value"] for point in average_bucket) / len(average_bucket)
+        range_start = int(bucket * bucket_width) + 1
+        range_end = min(int((bucket + 1) * bucket_width) + 1, len(points) - 1)
+        anchor = points[anchor_index]
+        selected_index = range_start
+        maximum_area = -1.0
+        for index in range(range_start, max(range_start + 1, range_end)):
+            point = points[index]
+            area = abs(
+                (anchor["step"] - average_x) * (point["value"] - anchor["value"])
+                - (anchor["step"] - point["step"]) * (average_y - anchor["value"])
+            )
+            if area > maximum_area:
+                maximum_area = area
+                selected_index = index
+        sampled.append(points[selected_index])
+        anchor_index = selected_index
+    sampled.append(points[-1])
+    return sampled
+
+
+class TuningStorage:
+    def __init__(self, path: Path):
+        self.path = path.expanduser().resolve()
+        self.path.parent.mkdir(parents=True, exist_ok=True)
+        self.local = threading.local()
+        self._migrate()
+
+    def connection(self) -> sqlite3.Connection:
+        connection = getattr(self.local, "connection", None)
+        if connection is None:
+            connection = sqlite3.connect(self.path, timeout=10, isolation_level=None)
+            connection.row_factory = sqlite3.Row
+            connection.execute("PRAGMA foreign_keys=ON")
+            connection.execute("PRAGMA journal_mode=WAL")
+            connection.execute("PRAGMA busy_timeout=10000")
+            self.local.connection = connection
+        return connection
+
+    @contextmanager
+    def transaction(self) -> Iterator[sqlite3.Connection]:
+        connection = self.connection()
+        connection.execute("BEGIN IMMEDIATE")
+        try:
+            yield connection
+            connection.execute("COMMIT")
+        except Exception:
+            connection.execute("ROLLBACK")
+            raise
+
+    def _migrate(self) -> None:
+        connection = self.connection()
+        connection.executescript(
+            """
+            CREATE TABLE IF NOT EXISTS schema_migrations(version INTEGER PRIMARY KEY);
+            CREATE TABLE IF NOT EXISTS sessions(
+              id TEXT PRIMARY KEY, state TEXT NOT NULL, mode TEXT NOT NULL,
+              created_at TEXT NOT NULL, updated_at TEXT NOT NULL,
+              config_json TEXT NOT NULL, objective_json TEXT NOT NULL,
+              message TEXT NOT NULL, current_trial_id TEXT, best_trial_id TEXT,
+              consecutive_no_improve INTEGER NOT NULL DEFAULT 0,
+              fallback_enabled INTEGER NOT NULL DEFAULT 0
+            );
+            CREATE TABLE IF NOT EXISTS trials(
+              id TEXT PRIMARY KEY,
+              session_id TEXT NOT NULL REFERENCES sessions(id) ON DELETE CASCADE,
+              number INTEGER NOT NULL, state TEXT NOT NULL, rung INTEGER NOT NULL,
+              target_iterations INTEGER NOT NULL, reward_config_json TEXT NOT NULL,
+              proposal_id TEXT, run_dir TEXT NOT NULL, checkpoint_path TEXT,
+              policy_path TEXT, evaluation_json TEXT, score REAL, eligible INTEGER,
+              created_at TEXT NOT NULL, started_at TEXT, ended_at TEXT, message TEXT NOT NULL,
+              UNIQUE(session_id, number, rung)
+            );
+            CREATE TABLE IF NOT EXISTS proposals(
+              id TEXT PRIMARY KEY,
+              session_id TEXT NOT NULL REFERENCES sessions(id) ON DELETE CASCADE,
+              base_trial_id TEXT, state TEXT NOT NULL, source TEXT NOT NULL,
+              patch_json TEXT NOT NULL, rationale TEXT NOT NULL,
+              expected_json TEXT, confidence REAL NOT NULL,
+              created_at TEXT NOT NULL, decided_at TEXT, feedback TEXT
+            );
+            CREATE TABLE IF NOT EXISTS metric_points(
+              trial_id TEXT NOT NULL REFERENCES trials(id) ON DELETE CASCADE,
+              tag TEXT NOT NULL, step INTEGER NOT NULL,
+              wall_time REAL NOT NULL, value REAL NOT NULL,
+              PRIMARY KEY(trial_id, tag, step)
+            );
+            CREATE TABLE IF NOT EXISTS audit_events(
+              id INTEGER PRIMARY KEY AUTOINCREMENT,
+              session_id TEXT NOT NULL REFERENCES sessions(id) ON DELETE CASCADE,
+              event_type TEXT NOT NULL, payload_json TEXT NOT NULL, created_at TEXT NOT NULL
+            );
+            CREATE TABLE IF NOT EXISTS presets(
+              id TEXT PRIMARY KEY, name TEXT NOT NULL UNIQUE, session_id TEXT NOT NULL,
+              trial_id TEXT NOT NULL, reward_config_json TEXT NOT NULL, created_at TEXT NOT NULL
+            );
+            CREATE INDEX IF NOT EXISTS idx_trials_session ON trials(session_id, number, rung);
+            CREATE INDEX IF NOT EXISTS idx_proposals_session ON proposals(session_id, created_at);
+            CREATE INDEX IF NOT EXISTS idx_metrics_trial_tag ON metric_points(trial_id, tag, step);
+            """
+        )
+        connection.execute(
+            "INSERT OR IGNORE INTO schema_migrations(version) VALUES (?)", (SCHEMA_VERSION,)
+        )
+
+    def recover_interrupted(self) -> None:
+        at = now_iso()
+        with self.transaction() as connection:
+            connection.execute(
+                "UPDATE trials SET state='interrupted', ended_at=?, "
+                "message='服务重启中断，等待显式恢复' "
+                "WHERE state IN ('training','evaluating')",
+                (at,),
+            )
+            connection.execute(
+                "UPDATE sessions SET state='interrupted', updated_at=?, "
+                "message='服务重启中断，可从完整 checkpoint 恢复' "
+                "WHERE state IN ('running','evaluating')",
+                (at,),
+            )
+
+    def create_session(self, mode: str, config: dict, objective: dict, fallback: bool) -> dict:
+        session_id, at = uuid.uuid4().hex, now_iso()
+        with self.transaction() as connection:
+            connection.execute(
+                "INSERT INTO sessions("
+                "id,state,mode,created_at,updated_at,config_json,objective_json,"
+                "message,fallback_enabled) "
+                "VALUES (?, 'queued', ?, ?, ?, ?, ?, '等待基线训练', ?)",
+                (session_id, mode, at, at, _json(config), _json(objective), int(fallback)),
+            )
+            connection.execute(
+                "INSERT INTO audit_events(session_id,event_type,payload_json,created_at) "
+                "VALUES (?,?,?,?)",
+                (session_id, "session_created", _json({"mode": mode}), at),
+            )
+        return self.get_session(session_id)
+
+    def _session(self, row: sqlite3.Row) -> dict:
+        return {
+            "id": row["id"],
+            "state": row["state"],
+            "mode": row["mode"],
+            "createdAt": row["created_at"],
+            "updatedAt": row["updated_at"],
+            "config": _decode(row["config_json"]),
+            "objectiveWeights": _decode(row["objective_json"]),
+            "message": row["message"],
+            "currentTrialId": row["current_trial_id"],
+            "bestTrialId": row["best_trial_id"],
+            "consecutiveNoImprove": row["consecutive_no_improve"],
+            "fallbackEnabled": bool(row["fallback_enabled"]),
+        }
+
+    def get_session(self, session_id: str) -> dict:
+        row = (
+            self.connection().execute("SELECT * FROM sessions WHERE id=?", (session_id,)).fetchone()
+        )
+        if row is None:
+            raise KeyError(session_id)
+        return self._session(row)
+
+    def list_sessions(self, limit: int = 50) -> list[dict]:
+        rows = (
+            self.connection()
+            .execute("SELECT * FROM sessions ORDER BY created_at DESC LIMIT ?", (limit,))
+            .fetchall()
+        )
+        return [self._session(row) for row in rows]
+
+    def update_session(self, session_id: str, **changes: Any) -> bool:
+        columns = {
+            "state": "state",
+            "message": "message",
+            "current_trial_id": "current_trial_id",
+            "best_trial_id": "best_trial_id",
+            "consecutive_no_improve": "consecutive_no_improve",
+        }
+        values, assignments = [], []
+        for key, value in changes.items():
+            if key not in columns:
+                raise ValueError(key)
+            assignments.append(f"{columns[key]}=?")
+            values.append(value)
+        assignments.append("updated_at=?")
+        values.extend((now_iso(), session_id))
+        cursor = self.connection().execute(
+            f"UPDATE sessions SET {', '.join(assignments)} WHERE id=?", values
+        )
+        return cursor.rowcount == 1
+
+    def create_trial(
+        self,
+        session_id: str,
+        number: int,
+        rung: int,
+        target: int,
+        reward_config: dict,
+        proposal_id: str | None,
+        run_dir: str,
+    ) -> dict:
+        trial_id, at = uuid.uuid4().hex, now_iso()
+        with self.transaction() as connection:
+            connection.execute(
+                "INSERT INTO trials("
+                "id,session_id,number,state,rung,target_iterations,reward_config_json,"
+                "proposal_id,run_dir,created_at,message) "
+                "VALUES (?,?,?,'queued',?,?,?,?,?,?,'等待训练')",
+                (
+                    trial_id,
+                    session_id,
+                    number,
+                    rung,
+                    target,
+                    _json(reward_config),
+                    proposal_id,
+                    run_dir,
+                    at,
+                ),
+            )
+            connection.execute(
+                "UPDATE sessions SET current_trial_id=?,updated_at=? WHERE id=?",
+                (trial_id, at, session_id),
+            )
+        return self.get_trial(trial_id)
+
+    def _trial(self, row: sqlite3.Row) -> dict:
+        return {
+            "id": row["id"],
+            "sessionId": row["session_id"],
+            "number": row["number"],
+            "state": row["state"],
+            "rung": row["rung"],
+            "targetIterations": row["target_iterations"],
+            "rewardConfig": _decode(row["reward_config_json"]),
+            "proposalId": row["proposal_id"],
+            "runDir": row["run_dir"],
+            "checkpointPath": row["checkpoint_path"],
+            "policyPath": row["policy_path"],
+            "evaluation": _decode(row["evaluation_json"]),
+            "score": row["score"],
+            "eligible": None if row["eligible"] is None else bool(row["eligible"]),
+            "createdAt": row["created_at"],
+            "startedAt": row["started_at"],
+            "endedAt": row["ended_at"],
+            "message": row["message"],
+        }
+
+    def get_trial(self, trial_id: str) -> dict:
+        row = self.connection().execute("SELECT * FROM trials WHERE id=?", (trial_id,)).fetchone()
+        if row is None:
+            raise KeyError(trial_id)
+        return self._trial(row)
+
+    def list_trials(self, session_id: str) -> list[dict]:
+        rows = (
+            self.connection()
+            .execute("SELECT * FROM trials WHERE session_id=? ORDER BY number,rung", (session_id,))
+            .fetchall()
+        )
+        return [self._trial(row) for row in rows]
+
+    def delete_trial(self, trial_id: str) -> bool:
+        cursor = self.connection().execute(
+            "DELETE FROM trials WHERE id=? AND state='interrupted'", (trial_id,)
+        )
+        return cursor.rowcount == 1
+
+    def update_trial(self, trial_id: str, **changes: Any) -> bool:
+        columns = {
+            "state": "state",
+            "message": "message",
+            "checkpoint_path": "checkpoint_path",
+            "policy_path": "policy_path",
+            "score": "score",
+            "eligible": "eligible",
+            "started_at": "started_at",
+            "ended_at": "ended_at",
+            "evaluation": "evaluation_json",
+        }
+        values, assignments = [], []
+        for key, value in changes.items():
+            if key not in columns:
+                raise ValueError(key)
+            if key == "evaluation":
+                value = _json(value)
+            if key == "eligible":
+                value = int(value)
+            assignments.append(f"{columns[key]}=?")
+            values.append(value)
+        values.append(trial_id)
+        cursor = self.connection().execute(
+            f"UPDATE trials SET {', '.join(assignments)} WHERE id=?", values
+        )
+        return cursor.rowcount == 1
+
+    def create_proposal(
+        self,
+        session_id: str,
+        base_trial_id: str | None,
+        patch: dict,
+        rationale: str,
+        expected: Any,
+        confidence: float,
+        source: str = "agent",
+    ) -> dict:
+        proposal_id, at = uuid.uuid4().hex, now_iso()
+        self.connection().execute(
+            "INSERT INTO proposals("
+            "id,session_id,base_trial_id,state,source,patch_json,rationale,"
+            "expected_json,confidence,created_at) "
+            "VALUES (?,?,?,'pending',?,?,?,?,?,?)",
+            (
+                proposal_id,
+                session_id,
+                base_trial_id,
+                source,
+                _json(patch),
+                rationale,
+                _json(expected),
+                confidence,
+                at,
+            ),
+        )
+        return self.get_proposal(proposal_id)
+
+    def _proposal(self, row: sqlite3.Row) -> dict:
+        return {
+            "id": row["id"],
+            "sessionId": row["session_id"],
+            "baseTrialId": row["base_trial_id"],
+            "state": row["state"],
+            "source": row["source"],
+            "patch": _decode(row["patch_json"]),
+            "rationale": row["rationale"],
+            "expectedImpact": _decode(row["expected_json"]),
+            "confidence": row["confidence"],
+            "createdAt": row["created_at"],
+            "decidedAt": row["decided_at"],
+            "feedback": row["feedback"],
+        }
+
+    def get_proposal(self, proposal_id: str) -> dict:
+        row = (
+            self.connection()
+            .execute("SELECT * FROM proposals WHERE id=?", (proposal_id,))
+            .fetchone()
+        )
+        if row is None:
+            raise KeyError(proposal_id)
+        return self._proposal(row)
+
+    def list_proposals(self, session_id: str) -> list[dict]:
+        rows = (
+            self.connection()
+            .execute(
+                "SELECT * FROM proposals WHERE session_id=? ORDER BY created_at", (session_id,)
+            )
+            .fetchall()
+        )
+        return [self._proposal(row) for row in rows]
+
+    def decide_proposal(
+        self, proposal_id: str, state: str, feedback: str | None, patch: dict | None = None
+    ) -> bool:
+        at = now_iso()
+        assignments, values = ["state=?", "feedback=?", "decided_at=?"], [state, feedback, at]
+        if patch is not None:
+            assignments.append("patch_json=?")
+            values.append(_json(patch))
+        values.extend((proposal_id,))
+        cursor = self.connection().execute(
+            f"UPDATE proposals SET {', '.join(assignments)} WHERE id=? AND state='pending'", values
+        )
+        return cursor.rowcount == 1
+
+    def insert_metrics(self, trial_id: str, points: list[tuple[str, int, float, float]]) -> None:
+        self.connection().executemany(
+            "INSERT INTO metric_points(trial_id,tag,step,wall_time,value) "
+            "VALUES (?,?,?,?,?) ON CONFLICT(trial_id,tag,step) DO UPDATE SET "
+            "wall_time=excluded.wall_time,value=excluded.value",
+            [(trial_id, *point) for point in points],
+        )
+
+    def metrics(
+        self, trial_id: str, tags: list[str] | None = None, max_points: int = 1000
+    ) -> list[dict]:
+        parameters: list[Any] = [trial_id]
+        clause = "trial_id=?"
+        if tags:
+            clause += f" AND tag IN ({','.join('?' for _ in tags)})"
+            parameters.extend(tags)
+        rows = (
+            self.connection()
+            .execute(
+                f"SELECT tag,step,wall_time,value FROM metric_points "
+                f"WHERE {clause} ORDER BY tag,step",
+                parameters,
+            )
+            .fetchall()
+        )
+        grouped: dict[str, list[dict]] = {}
+        for row in rows:
+            grouped.setdefault(row["tag"], []).append(
+                {"step": row["step"], "wallTime": row["wall_time"], "value": row["value"]}
+            )
+        series = []
+        for tag, values in grouped.items():
+            if len(values) > max_points:
+                values = _lttb(values, max_points)
+            series.append({"tag": tag, "points": values})
+        return series
+
+    def audit(self, session_id: str, event_type: str, payload: Any) -> None:
+        self.connection().execute(
+            "INSERT INTO audit_events(session_id,event_type,payload_json,created_at) "
+            "VALUES (?,?,?,?)",
+            (session_id, event_type, _json(payload), now_iso()),
+        )
+
+    def audit_events(self, session_id: str) -> list[dict]:
+        rows = (
+            self.connection()
+            .execute("SELECT * FROM audit_events WHERE session_id=? ORDER BY id", (session_id,))
+            .fetchall()
+        )
+        return [
+            {
+                "id": row["id"],
+                "type": row["event_type"],
+                "payload": _decode(row["payload_json"]),
+                "createdAt": row["created_at"],
+            }
+            for row in rows
+        ]
+
+    def save_preset(self, name: str, session_id: str, trial_id: str, reward_config: dict) -> dict:
+        preset_id, at = uuid.uuid4().hex, now_iso()
+        self.connection().execute(
+            "INSERT INTO presets(id,name,session_id,trial_id,reward_config_json,created_at) "
+            "VALUES (?,?,?,?,?,?)",
+            (preset_id, name, session_id, trial_id, _json(reward_config), at),
+        )
+        return {
+            "id": preset_id,
+            "name": name,
+            "sessionId": session_id,
+            "trialId": trial_id,
+            "rewardConfig": reward_config,
+            "createdAt": at,
+        }
+
+    def get_preset(self, preset_id: str) -> dict:
+        row = self.connection().execute("SELECT * FROM presets WHERE id=?", (preset_id,)).fetchone()
+        if row is None:
+            raise KeyError(preset_id)
+        return {
+            "id": row["id"],
+            "name": row["name"],
+            "sessionId": row["session_id"],
+            "trialId": row["trial_id"],
+            "rewardConfig": _decode(row["reward_config_json"]),
+            "createdAt": row["created_at"],
+        }
+
+    def list_presets(self) -> list[dict]:
+        rows = (
+            self.connection().execute("SELECT * FROM presets ORDER BY created_at DESC").fetchall()
+        )
+        return [
+            {
+                "id": row["id"],
+                "name": row["name"],
+                "sessionId": row["session_id"],
+                "trialId": row["trial_id"],
+                "rewardConfig": _decode(row["reward_config_json"]),
+                "createdAt": row["created_at"],
+            }
+            for row in rows
+        ]

training_server/tuning/study.py

@@ -0,0 +1,55 @@
+"""Lazy Optuna study integration used for durable trial history and pruning metadata."""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+
+class OptunaStudies:
+    def __init__(self, root: Path):
+        self.path = (root / "optuna.sqlite3").resolve()
+        self.url = f"sqlite:///{self.path}"
+
+    def _study(self, session_id: str):
+        import optuna
+
+        optuna.logging.set_verbosity(optuna.logging.WARNING)
+        return optuna.create_study(
+            study_name=f"reward-tuning-{session_id}",
+            storage=self.url,
+            direction="maximize",
+            load_if_exists=True,
+            pruner=optuna.pruners.SuccessiveHalvingPruner(
+                min_resource=300, reduction_factor=3, min_early_stopping_rate=0
+            ),
+        )
+
+    def record(
+        self,
+        session_id: str,
+        reward_config: dict[str, Any],
+        score: float,
+        eligible: bool,
+        rung: int,
+    ) -> int:
+        """Record an externally proposed Agent config through Optuna ask/tell."""
+        import optuna
+
+        study = self._study(session_id)
+        trial = study.ask()
+        trial.set_user_attr("reward_config", reward_config)
+        trial.set_user_attr("eligible", eligible)
+        trial.set_user_attr("rung", rung)
+        state = optuna.trial.TrialState.COMPLETE if eligible else optuna.trial.TrialState.PRUNED
+        study.tell(trial, score if eligible else None, state=state)
+        return trial.number
+
+    def summary(self, session_id: str) -> dict[str, Any]:
+        study = self._study(session_id)
+        completed = [trial for trial in study.trials if trial.value is not None]
+        return {
+            "studyName": study.study_name,
+            "trialCount": len(study.trials),
+            "bestValue": max((trial.value for trial in completed), default=None),
+        }

training_server/tuning/tensorboard.py

@@ -0,0 +1,35 @@
+"""TensorBoard scalar ingestion with optional dependency isolation."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from .storage import TuningStorage
+
+
+class TensorboardUnavailable(RuntimeError):
+    pass
+
+
+def ingest_scalars(storage: TuningStorage, trial_id: str, log_dir: Path) -> int:
+    """Reload all scalar events and idempotently upsert them into SQLite."""
+    try:
+        from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
+    except ImportError as error:
+        raise TensorboardUnavailable(
+            "缺少 tensorboard，请安装 training_server/requirements.txt"
+        ) from error
+    if not log_dir.is_dir():
+        return 0
+    accumulator = EventAccumulator(str(log_dir), size_guidance={"scalars": 0})
+    try:
+        accumulator.Reload()
+    except (OSError, ValueError):
+        return 0
+    points: list[tuple[str, int, float, float]] = []
+    for tag in accumulator.Tags().get("scalars", []):
+        for event in accumulator.Scalars(tag):
+            points.append((tag, int(event.step), float(event.wall_time), float(event.value)))
+    if points:
+        storage.insert_metrics(trial_id, points)
+    return len(points)