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import random
from pathlib import Path
from typing import Any
import numpy as np
from district_llm.guided_control import DistrictGuidedLocalController
from district_llm.schema import DistrictAction
from district_llm.summary_builder import DistrictStateSummaryBuilder
from district_llm.teachers import build_teacher, parse_teacher_spec
from env.observation_builder import ObservationConfig
from env.reward import RewardConfig
from env.traffic_env import EnvConfig, TrafficEnv
from training.cityflow_dataset import CityFlowDataset
class OpenEnvTrafficWrapper:
"""
OpenEnv-style district environment backed by the current DQN local stack.
External action:
- a dict of district-level directives keyed by district_id
Internal execution:
- the shared DQN (or a baseline fallback) produces low-level actions
- district directives bias those low-level actions over a slower district window
"""
def __init__(
self,
generated_root: str | Path = "data/generated",
splits_root: str | Path = "data/splits",
split: str = "train",
controller_spec: str | None = None,
district_decision_interval: int = 10,
seed: int = 7,
):
self.dataset = CityFlowDataset(
generated_root=generated_root,
splits_root=splits_root,
)
self.dataset.generate_default_splits()
self.split = split
self.rng = random.Random(seed)
self.district_decision_interval = int(district_decision_interval)
self.summary_builder = DistrictStateSummaryBuilder()
default_checkpoint = Path("artifacts/dqn_shared/best_validation.pt")
if controller_spec is None:
controller_spec = (
f"rl_checkpoint={default_checkpoint}"
if default_checkpoint.exists()
else "queue_greedy"
)
controller_type, checkpoint = parse_teacher_spec(controller_spec)
try:
self.teacher = build_teacher(
controller_type=controller_type,
checkpoint=checkpoint,
seed=seed,
)
except ImportError:
if controller_spec != "queue_greedy":
self.teacher = build_teacher(
controller_type="queue_greedy",
checkpoint=None,
seed=seed,
)
else:
raise
self.guided_controller = DistrictGuidedLocalController(base_teacher=self.teacher)
self.env_config = self.teacher.env_config or self._default_env_config()
self.env: TrafficEnv | None = None
self.current_scenario_spec = None
self.last_obs: dict[str, Any] | None = None
self.last_info: dict[str, Any] | None = None
self.last_summaries: dict[str, Any] = {}
def reset(
self,
seed: int | None = None,
city_id: str | None = None,
scenario_name: str | None = None,
) -> dict[str, Any]:
scenario_spec = (
self.dataset.build_scenario_spec(city_id, scenario_name)
if city_id and scenario_name
else self.dataset.sample_scenario(
split_name=self.split,
rng=self.rng,
city_id=city_id,
scenario_name=scenario_name,
)
)
self.current_scenario_spec = scenario_spec
self.env = TrafficEnv(
city_id=scenario_spec.city_id,
scenario_name=scenario_spec.scenario_name,
city_dir=scenario_spec.city_dir,
scenario_dir=scenario_spec.scenario_dir,
config_path=scenario_spec.config_path,
roadnet_path=scenario_spec.roadnet_path,
district_map_path=scenario_spec.district_map_path,
metadata_path=scenario_spec.metadata_path,
env_config=self.env_config,
)
self.summary_builder.reset()
self.last_obs = self.env.reset(seed=seed)
self.last_summaries = self.summary_builder.build_all(self.env, self.last_obs)
self.last_info = {
"seed": seed,
"city_id": scenario_spec.city_id,
"scenario_name": scenario_spec.scenario_name,
"controller_type": self.teacher.metadata.controller_type,
"controller_family": self.teacher.metadata.controller_family,
"teacher_algorithm": self.teacher.metadata.teacher_algorithm,
"district_decision_interval": self.district_decision_interval,
}
return {
"observation": self._build_observation_payload(),
"info": self.last_info,
}
def step(self, action: dict[str, Any]) -> dict[str, Any]:
if self.env is None or self.last_obs is None:
self.reset(seed=None)
assert self.env is not None
district_actions = self._parse_district_actions(action.get("district_actions", {}))
done = False
reward_total = 0.0
steps_executed = 0
info: dict[str, Any] = {}
for _ in range(self.district_decision_interval):
local_actions = self.guided_controller.act(
observation_batch=self.last_obs,
district_actions=district_actions,
)
next_obs, rewards, done, info = self.env.step(local_actions)
reward_total += float(np.asarray(rewards, dtype=np.float32).mean())
self.last_obs = next_obs
steps_executed += 1
if done:
break
self.last_summaries = self.summary_builder.build_all(self.env, self.last_obs)
self.last_info = {
**info,
"controller_type": self.teacher.metadata.controller_type,
"controller_family": self.teacher.metadata.controller_family,
"teacher_algorithm": self.teacher.metadata.teacher_algorithm,
"steps_executed": steps_executed,
"district_actions": {
district_id: directive.to_dict()
for district_id, directive in district_actions.items()
},
}
return {
"observation": self._build_observation_payload(),
"reward": float(reward_total),
"done": bool(done),
"truncated": False,
"info": self.last_info,
}
def state(self) -> dict[str, Any]:
return {
"state": {
"scenario": (
None
if self.current_scenario_spec is None
else {
"city_id": self.current_scenario_spec.city_id,
"scenario_name": self.current_scenario_spec.scenario_name,
}
),
"controller": self.teacher.metadata.to_dict(),
"district_decision_interval": self.district_decision_interval,
"district_summaries": {
district_id: summary.to_dict()
for district_id, summary in self.last_summaries.items()
},
"last_info": self.last_info or {},
}
}
def health(self) -> dict[str, Any]:
return {
"status": "ok",
"message": "DistrictFlow OpenEnv wrapper is running.",
}
def _build_observation_payload(self) -> dict[str, Any]:
if self.env is None or self.last_obs is None:
return {"district_summaries": {}}
return {
"city_id": self.env.city_id,
"scenario_name": self.env.scenario_name,
"decision_step": int(self.last_obs["decision_step"]),
"sim_time": int(self.last_obs["sim_time"]),
"district_summaries": {
district_id: summary.to_dict()
for district_id, summary in self.last_summaries.items()
},
}
def _parse_district_actions(self, payload: dict[str, Any]) -> dict[str, DistrictAction]:
if self.env is None:
return {}
parsed: dict[str, DistrictAction] = {}
for district_id in self.env.districts:
raw_action = payload.get(district_id)
if raw_action is None:
parsed[district_id] = DistrictAction.default_hold(
duration_steps=self.district_decision_interval
)
continue
try:
parsed[district_id] = DistrictAction.from_dict(raw_action)
except Exception:
parsed[district_id] = DistrictAction.default_hold(
duration_steps=self.district_decision_interval
)
return parsed
@staticmethod
def _default_env_config() -> EnvConfig:
return EnvConfig(
simulator_interval=1,
decision_interval=5,
min_green_time=10,
thread_num=1,
max_episode_seconds=None,
observation=ObservationConfig(),
reward=RewardConfig(variant="wait_queue_throughput"),
)
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