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import argparse
import json
from dataclasses import asdict, dataclass
from datetime import datetime, timezone
from pathlib import Path
from typing import Any
import sys
from tqdm.auto import tqdm
REPO_ROOT = Path(__file__).resolve().parents[1]
if str(REPO_ROOT) not in sys.path:
sys.path.insert(0, str(REPO_ROOT))
from district_llm.heuristic_guidance import HeuristicGuidanceConfig
from district_llm.inference import DistrictLLMInference
from district_llm.repair import RepairConfig
from district_llm.rl_guidance_wrapper import (
BIAS_DECAY_SCHEDULES,
GATING_MODES,
DistrictGuidedRLController,
FixedRLPolicyAdapter,
GuidanceInfluenceConfig,
HeuristicGuidanceProvider,
LLMGuidanceProvider,
guidance_config_payload,
)
from district_llm.summary_builder import DistrictStateSummaryBuilder
from env.traffic_env import EnvConfig
from scripts.eval_rl_guidance_ablation import (
build_episode_plans,
default_env_config,
distribution_summary,
env_config_to_payload,
resolve_scenario_specs,
run_episode,
safe_float,
try_write_parquet,
write_csv_rows,
write_json,
write_jsonl,
)
from training.cityflow_dataset import CityFlowDataset
PRESET_CHOICES: tuple[str, ...] = (
"strength_only",
"strength_and_targets",
"strength_targets_gating",
"full_conservative",
)
DEFAULT_CITIES: tuple[str, ...] = ("city_0001",)
DEFAULT_SCENARIOS: tuple[str, ...] = ("normal",)
@dataclass(frozen=True)
class SweepConfigSpec:
config_id: str
description: str
wrapper_mode: str
bias_strength: float
target_only_bias_strength: float
corridor_bias_strength: float
max_intersections_affected: int
guidance_persistence_steps: int
guidance_refresh_steps: int
max_guidance_duration: int
gating_mode: str
min_avg_queue_for_guidance: float
min_queue_imbalance_for_guidance: float
require_incident_or_spillback: bool
allow_guidance_in_normal_conditions: bool
enable_bias_decay: bool
bias_decay_schedule: str
fallback_policy: str
is_reference: bool = False
def to_influence_config(self) -> GuidanceInfluenceConfig:
return GuidanceInfluenceConfig(
wrapper_mode=self.wrapper_mode,
bias_strength=self.bias_strength,
target_only_bias_strength=self.target_only_bias_strength,
corridor_bias_strength=self.corridor_bias_strength,
max_intersections_affected=self.max_intersections_affected,
guidance_refresh_steps=self.guidance_refresh_steps,
guidance_persistence_steps=self.guidance_persistence_steps,
max_guidance_duration=self.max_guidance_duration,
apply_global_bias=False,
apply_target_only=True,
gating_mode=self.gating_mode,
min_avg_queue_for_guidance=self.min_avg_queue_for_guidance,
min_queue_imbalance_for_guidance=self.min_queue_imbalance_for_guidance,
require_incident_or_spillback=self.require_incident_or_spillback,
allow_guidance_in_normal_conditions=self.allow_guidance_in_normal_conditions,
enable_bias_decay=self.enable_bias_decay,
bias_decay_schedule=self.bias_decay_schedule,
fallback_policy=self.fallback_policy,
log_guidance_debug=False,
).validate()
def to_dict(self) -> dict[str, Any]:
return asdict(self)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description=(
"Cheap paired hyperparameter sweep for the fixed RL + district LLM wrapper. "
"The RL checkpoint and LLM checkpoint stay fixed; only inference-time wrapper "
"hyperparameters are varied."
)
)
parser.add_argument("--rl-checkpoint", required=True)
parser.add_argument("--llm-model-path", required=True)
parser.add_argument("--generated-root", default="data/generated")
parser.add_argument("--splits-root", default="data/splits")
parser.add_argument("--split", default="val", choices=("train", "val", "test"))
parser.add_argument("--cities", nargs="+", default=list(DEFAULT_CITIES))
parser.add_argument("--scenarios", nargs="+", default=list(DEFAULT_SCENARIOS))
parser.add_argument("--seeds", nargs="+", type=int, default=[7, 11, 13])
parser.add_argument("--episodes-per-seed", type=int, default=1)
parser.add_argument(
"--max-episode-seconds",
type=int,
default=300,
help="Cheap default horizon for wrapper tuning sweeps.",
)
parser.add_argument(
"--preset",
choices=PRESET_CHOICES,
default="strength_targets_gating",
)
parser.add_argument("--guidance-refresh-steps", type=int, default=10)
parser.add_argument("--max-guidance-duration", type=int, default=10)
parser.add_argument("--queue-threshold", type=float, default=150.0)
parser.add_argument("--imbalance-threshold", type=float, default=20.0)
parser.add_argument("--max-new-tokens", type=int, default=128)
parser.add_argument("--device", default=None)
parser.add_argument("--output-dir", default="artifacts/rl_llm_wrapper_sweep")
parser.add_argument(
"--allow-only-visible-candidates",
action=argparse.BooleanOptionalAction,
default=True,
)
parser.add_argument("--max-target-intersections", type=int, default=3)
parser.add_argument(
"--fallback-on-empty-targets",
action=argparse.BooleanOptionalAction,
default=True,
)
parser.add_argument(
"--fallback-mode",
choices=("heuristic", "hold", "none"),
default="heuristic",
)
parser.add_argument(
"--fallback-policy",
choices=("no_op", "hold_previous", "heuristic_weak"),
default="no_op",
)
parser.add_argument(
"--save-step-metrics",
action=argparse.BooleanOptionalAction,
default=False,
)
parser.add_argument(
"--save-guidance-traces",
action=argparse.BooleanOptionalAction,
default=False,
)
parser.add_argument(
"--bias-decay-schedule",
choices=BIAS_DECAY_SCHEDULES,
default="linear",
)
return parser.parse_args()
def main() -> None:
args = parse_args()
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
seeded_config_root = output_dir / "seeded_configs"
seeded_config_root.mkdir(parents=True, exist_ok=True)
dataset = CityFlowDataset(
generated_root=args.generated_root,
splits_root=args.splits_root,
)
dataset.generate_default_splits()
scenario_specs = resolve_scenario_specs(dataset=dataset, args=args)
episode_plans = build_episode_plans(
scenario_specs=scenario_specs,
seeds=args.seeds,
num_episodes=args.episodes_per_seed,
seeded_config_root=seeded_config_root,
)
sweep_configs = build_sweep_configs(args)
rl_policy = FixedRLPolicyAdapter(
checkpoint_path=args.rl_checkpoint,
device=args.device,
)
env_config = rl_policy.env_config or default_env_config()
if args.max_episode_seconds is not None:
env_config = EnvConfig(
simulator_interval=env_config.simulator_interval,
decision_interval=env_config.decision_interval,
min_green_time=env_config.min_green_time,
thread_num=env_config.thread_num,
max_episode_seconds=int(args.max_episode_seconds),
observation=env_config.observation,
reward=env_config.reward,
)
rl_only_controller = build_rl_only_controller(
rl_policy=rl_policy,
guidance_refresh_steps=args.guidance_refresh_steps,
max_guidance_duration=args.max_guidance_duration,
)
guided_controllers = build_guided_controllers(
args=args,
rl_policy=rl_policy,
sweep_configs=sweep_configs,
)
sweep_rows: list[dict[str, Any]] = []
paired_rows: list[dict[str, Any]] = []
rl_only_rows: list[dict[str, Any]] = []
step_rows: list[dict[str, Any]] = []
guidance_trace_rows: list[dict[str, Any]] = []
total_runs = len(episode_plans) * (1 + len(sweep_configs))
progress = tqdm(total=total_runs, desc="RL+LLM wrapper sweep", unit="run")
try:
for plan_index, plan in enumerate(episode_plans, start=1):
progress.set_postfix_str(
f"rl_only city={plan.city_id} scenario={plan.scenario} seed={plan.seed}"
)
rl_only_row, rl_only_step_rows, rl_only_trace_rows = run_episode(
plan=plan,
mode_label="rl_only",
controller=rl_only_controller,
env_config=env_config,
save_step_metrics=args.save_step_metrics,
save_guidance_traces=args.save_guidance_traces,
show_step_progress=False,
)
rl_only_row = augment_rl_only_row(rl_only_row)
rl_only_rows.append(rl_only_row)
if args.save_step_metrics:
step_rows.extend(
augment_auxiliary_rows(
rows=rl_only_step_rows,
config_id="rl_only",
config_spec=None,
)
)
if args.save_guidance_traces:
guidance_trace_rows.extend(
augment_auxiliary_rows(
rows=rl_only_trace_rows,
config_id="rl_only",
config_spec=None,
)
)
progress.update(1)
for config in sweep_configs:
controller = guided_controllers[config.config_id]
progress.set_postfix_str(
f"{config.config_id} city={plan.city_id} scenario={plan.scenario} seed={plan.seed}"
)
episode_row, mode_step_rows, mode_trace_rows = run_episode(
plan=plan,
mode_label=config.config_id,
controller=controller,
env_config=env_config,
save_step_metrics=args.save_step_metrics,
save_guidance_traces=args.save_guidance_traces,
show_step_progress=False,
)
episode_row = augment_guided_row(episode_row, config)
sweep_rows.append(episode_row)
paired_rows.append(build_paired_row(guided_row=episode_row, rl_only_row=rl_only_row))
if args.save_step_metrics:
step_rows.extend(
augment_auxiliary_rows(
rows=mode_step_rows,
config_id=config.config_id,
config_spec=config,
)
)
if args.save_guidance_traces:
guidance_trace_rows.extend(
augment_auxiliary_rows(
rows=mode_trace_rows,
config_id=config.config_id,
config_spec=config,
)
)
progress.update(1)
tqdm.write(
"[sweep-plan] "
f"{plan_index}/{len(episode_plans)} "
f"city={plan.city_id} scenario={plan.scenario} seed={plan.seed} complete"
)
finally:
progress.close()
ranking_rows = build_config_rankings(
paired_rows=paired_rows,
sweep_configs=sweep_configs,
)
summary_report = build_summary_report(
paired_rows=paired_rows,
ranking_rows=ranking_rows,
rl_only_rows=rl_only_rows,
args=args,
sweep_configs=sweep_configs,
)
config_payload = build_config_payload(
args=args,
env_config=env_config,
episode_plans=episode_plans,
sweep_configs=sweep_configs,
)
write_json(output_dir / "config.json", config_payload)
write_csv_rows(output_dir / "sweep_results.csv", sweep_rows)
write_jsonl(output_dir / "sweep_results.jsonl", sweep_rows)
try_write_parquet(output_dir / "sweep_results.parquet", sweep_rows)
write_csv_rows(output_dir / "paired_episode_metrics.csv", paired_rows)
write_jsonl(output_dir / "paired_episode_metrics.jsonl", paired_rows)
try_write_parquet(output_dir / "paired_episode_metrics.parquet", paired_rows)
write_csv_rows(output_dir / "rl_only_episode_metrics.csv", rl_only_rows)
write_json(output_dir / "ranking.json", ranking_rows)
write_json(output_dir / "summary_report.json", summary_report)
if args.save_step_metrics:
write_csv_rows(output_dir / "step_metrics.csv", step_rows)
write_jsonl(output_dir / "step_metrics.jsonl", step_rows)
try_write_parquet(output_dir / "step_metrics.parquet", step_rows)
if args.save_guidance_traces:
write_jsonl(output_dir / "guidance_traces.jsonl", guidance_trace_rows)
print(json.dumps(summary_report, indent=2, sort_keys=True))
def build_rl_only_controller(
rl_policy: FixedRLPolicyAdapter,
guidance_refresh_steps: int,
max_guidance_duration: int,
) -> DistrictGuidedRLController:
return DistrictGuidedRLController(
policy=rl_policy,
mode_source="rl_only",
summary_builder=None,
guidance_provider=None,
influence_config=GuidanceInfluenceConfig(
wrapper_mode="no_op",
bias_strength=0.0,
target_only_bias_strength=0.0,
corridor_bias_strength=0.0,
max_intersections_affected=1,
guidance_refresh_steps=guidance_refresh_steps,
guidance_persistence_steps=1,
max_guidance_duration=max_guidance_duration,
gating_mode="always_on",
enable_bias_decay=False,
fallback_policy="no_op",
),
heuristic_provider=None,
)
def build_guided_controllers(
args: argparse.Namespace,
rl_policy: FixedRLPolicyAdapter,
sweep_configs: list[SweepConfigSpec],
) -> dict[str, DistrictGuidedRLController]:
repair_config = RepairConfig(
allow_only_visible_candidates=args.allow_only_visible_candidates,
max_target_intersections=args.max_target_intersections,
fallback_on_empty_targets=args.fallback_on_empty_targets,
fallback_mode=args.fallback_mode,
)
llm_inference = DistrictLLMInference(
model_name_or_path=args.llm_model_path,
device=args.device,
repair_config=repair_config,
)
heuristic_provider = HeuristicGuidanceProvider(
config=HeuristicGuidanceConfig(
max_target_intersections=args.max_target_intersections,
)
)
llm_provider = LLMGuidanceProvider(
inference=llm_inference,
max_new_tokens=args.max_new_tokens,
)
controllers: dict[str, DistrictGuidedRLController] = {}
for config in sweep_configs:
controllers[config.config_id] = DistrictGuidedRLController(
policy=rl_policy,
mode_source="rl_llm",
summary_builder=DistrictStateSummaryBuilder(
top_k=3,
candidate_limit=max(6, int(args.max_target_intersections)),
),
guidance_provider=llm_provider,
influence_config=config.to_influence_config(),
heuristic_provider=heuristic_provider,
)
return controllers
def build_sweep_configs(args: argparse.Namespace) -> list[SweepConfigSpec]:
configs: list[SweepConfigSpec] = [
build_baseline_reference_config(args),
]
if args.preset == "strength_only":
for bias_strength in (0.025, 0.05, 0.075, 0.10):
configs.append(
build_target_only_soft_config(
args=args,
bias_strength=bias_strength,
max_intersections_affected=2,
guidance_persistence_steps=5,
gating_mode="queue_or_imbalance",
enable_bias_decay=False,
)
)
elif args.preset == "strength_and_targets":
for bias_strength in (0.025, 0.05, 0.075, 0.10):
for max_intersections_affected in (1, 2):
configs.append(
build_target_only_soft_config(
args=args,
bias_strength=bias_strength,
max_intersections_affected=max_intersections_affected,
guidance_persistence_steps=5,
gating_mode="queue_or_imbalance",
enable_bias_decay=False,
)
)
elif args.preset == "strength_targets_gating":
for bias_strength in (0.025, 0.05, 0.075):
for max_intersections_affected in (1, 2):
for gating_mode in ("always_on", "incident_or_spillback", "queue_or_imbalance"):
configs.append(
build_target_only_soft_config(
args=args,
bias_strength=bias_strength,
max_intersections_affected=max_intersections_affected,
guidance_persistence_steps=5,
gating_mode=gating_mode,
enable_bias_decay=False,
)
)
else:
for bias_strength in (0.025, 0.05, 0.075):
for max_intersections_affected in (1, 2):
for gating_mode, guidance_persistence_steps, enable_bias_decay in (
("queue_or_imbalance", 5, False),
("queue_or_imbalance", 10, True),
("incident_or_spillback", 5, False),
("incident_or_spillback", 10, True),
):
configs.append(
build_target_only_soft_config(
args=args,
bias_strength=bias_strength,
max_intersections_affected=max_intersections_affected,
guidance_persistence_steps=guidance_persistence_steps,
gating_mode=gating_mode,
enable_bias_decay=enable_bias_decay,
)
)
return dedupe_sweep_configs(configs)
def build_baseline_reference_config(args: argparse.Namespace) -> SweepConfigSpec:
return SweepConfigSpec(
config_id="baseline_current_soft",
description="Current rl_llm + target_only_soft reference config from the smoke runs.",
wrapper_mode="target_only_soft",
bias_strength=0.12,
target_only_bias_strength=0.18,
corridor_bias_strength=0.05,
max_intersections_affected=3,
guidance_persistence_steps=3,
guidance_refresh_steps=args.guidance_refresh_steps,
max_guidance_duration=max(args.max_guidance_duration, 3),
gating_mode="always_on",
min_avg_queue_for_guidance=args.queue_threshold,
min_queue_imbalance_for_guidance=args.imbalance_threshold,
require_incident_or_spillback=False,
allow_guidance_in_normal_conditions=True,
enable_bias_decay=True,
bias_decay_schedule=args.bias_decay_schedule,
fallback_policy=args.fallback_policy,
is_reference=True,
)
def build_target_only_soft_config(
args: argparse.Namespace,
bias_strength: float,
max_intersections_affected: int,
guidance_persistence_steps: int,
gating_mode: str,
enable_bias_decay: bool,
) -> SweepConfigSpec:
target_only_bias_strength = bias_strength
corridor_bias_strength = 0.5 * bias_strength
config_id = (
f"bs{format_float_token(bias_strength)}"
f"_aff{int(max_intersections_affected)}"
f"_gate{gating_mode_token(gating_mode)}"
f"_p{int(guidance_persistence_steps)}"
f"_decay{int(enable_bias_decay)}"
)
return SweepConfigSpec(
config_id=config_id,
description=(
"Curated conservative target_only_soft sweep config with locally tied target/corridor "
"bias strengths."
),
wrapper_mode="target_only_soft",
bias_strength=float(bias_strength),
target_only_bias_strength=float(target_only_bias_strength),
corridor_bias_strength=float(corridor_bias_strength),
max_intersections_affected=int(max_intersections_affected),
guidance_persistence_steps=int(guidance_persistence_steps),
guidance_refresh_steps=int(args.guidance_refresh_steps),
max_guidance_duration=max(int(args.max_guidance_duration), int(guidance_persistence_steps)),
gating_mode=gating_mode,
min_avg_queue_for_guidance=float(args.queue_threshold),
min_queue_imbalance_for_guidance=float(args.imbalance_threshold),
require_incident_or_spillback=False,
allow_guidance_in_normal_conditions=(gating_mode == "always_on"),
enable_bias_decay=bool(enable_bias_decay),
bias_decay_schedule=args.bias_decay_schedule,
fallback_policy=args.fallback_policy,
is_reference=False,
)
def dedupe_sweep_configs(configs: list[SweepConfigSpec]) -> list[SweepConfigSpec]:
deduped: list[SweepConfigSpec] = []
seen_ids: set[str] = set()
for config in configs:
if config.config_id in seen_ids:
continue
deduped.append(config)
seen_ids.add(config.config_id)
return deduped
def augment_rl_only_row(row: dict[str, Any]) -> dict[str, Any]:
payload = dict(row)
payload.update(
{
"config_id": "rl_only",
"description": "Fixed RL policy with no district guidance.",
"is_reference": True,
"bias_strength": 0.0,
"target_only_bias_strength": 0.0,
"corridor_bias_strength": 0.0,
"max_intersections_affected": 0,
"guidance_persistence_steps": 0,
"guidance_refresh_steps": 0,
"max_guidance_duration": 0,
"gating_mode": "always_on",
"min_avg_queue_for_guidance": 0.0,
"min_queue_imbalance_for_guidance": 0.0,
"require_incident_or_spillback": False,
"allow_guidance_in_normal_conditions": True,
"enable_bias_decay": False,
"bias_decay_schedule": "linear",
}
)
return payload
def augment_guided_row(row: dict[str, Any], config: SweepConfigSpec) -> dict[str, Any]:
payload = dict(row)
payload.update(
{
"config_id": config.config_id,
"description": config.description,
"is_reference": bool(config.is_reference),
"bias_strength": float(config.bias_strength),
"target_only_bias_strength": float(config.target_only_bias_strength),
"corridor_bias_strength": float(config.corridor_bias_strength),
"max_intersections_affected": int(config.max_intersections_affected),
"guidance_persistence_steps": int(config.guidance_persistence_steps),
"guidance_refresh_steps": int(config.guidance_refresh_steps),
"max_guidance_duration": int(config.max_guidance_duration),
"gating_mode": config.gating_mode,
"min_avg_queue_for_guidance": float(config.min_avg_queue_for_guidance),
"min_queue_imbalance_for_guidance": float(config.min_queue_imbalance_for_guidance),
"require_incident_or_spillback": bool(config.require_incident_or_spillback),
"allow_guidance_in_normal_conditions": bool(config.allow_guidance_in_normal_conditions),
"enable_bias_decay": bool(config.enable_bias_decay),
"bias_decay_schedule": config.bias_decay_schedule,
}
)
return payload
def augment_auxiliary_rows(
rows: list[dict[str, Any]],
config_id: str,
config_spec: SweepConfigSpec | None,
) -> list[dict[str, Any]]:
augmented: list[dict[str, Any]] = []
for row in rows:
payload = dict(row)
payload["config_id"] = config_id
payload["is_reference"] = bool(config_spec.is_reference) if config_spec is not None else False
if config_spec is not None:
payload["gating_mode"] = config_spec.gating_mode
payload["bias_strength"] = float(config_spec.bias_strength)
payload["max_intersections_affected"] = int(config_spec.max_intersections_affected)
payload["guidance_persistence_steps"] = int(config_spec.guidance_persistence_steps)
payload["enable_bias_decay"] = bool(config_spec.enable_bias_decay)
augmented.append(payload)
return augmented
def build_paired_row(guided_row: dict[str, Any], rl_only_row: dict[str, Any]) -> dict[str, Any]:
paired_row = dict(guided_row)
paired_row.update(
{
"rl_only_total_return": safe_float(rl_only_row.get("total_return")),
"rl_only_avg_queue": safe_float(rl_only_row.get("avg_queue")),
"rl_only_avg_wait": safe_float(rl_only_row.get("avg_wait")),
"rl_only_throughput": safe_float(rl_only_row.get("throughput")),
"rl_only_travel_time": safe_float(rl_only_row.get("travel_time")),
"total_return_delta_vs_rl_only": safe_float(guided_row.get("total_return"))
- safe_float(rl_only_row.get("total_return")),
"avg_queue_delta_vs_rl_only": safe_float(guided_row.get("avg_queue"))
- safe_float(rl_only_row.get("avg_queue")),
"avg_wait_delta_vs_rl_only": safe_float(guided_row.get("avg_wait"))
- safe_float(rl_only_row.get("avg_wait")),
"throughput_delta_vs_rl_only": safe_float(guided_row.get("throughput"))
- safe_float(rl_only_row.get("throughput")),
"travel_time_delta_vs_rl_only": safe_float(guided_row.get("travel_time"))
- safe_float(rl_only_row.get("travel_time")),
}
)
return paired_row
def build_config_rankings(
paired_rows: list[dict[str, Any]],
sweep_configs: list[SweepConfigSpec],
) -> list[dict[str, Any]]:
rows_by_config = {
config.config_id: [row for row in paired_rows if row["config_id"] == config.config_id]
for config in sweep_configs
}
rankings: list[dict[str, Any]] = []
config_lookup = {config.config_id: config for config in sweep_configs}
for config_id, rows in rows_by_config.items():
if not rows:
continue
config = config_lookup[config_id]
summary = {
"config_id": config_id,
"description": config.description,
"is_reference": bool(config.is_reference),
"wrapper_mode": config.wrapper_mode,
"bias_strength": float(config.bias_strength),
"target_only_bias_strength": float(config.target_only_bias_strength),
"corridor_bias_strength": float(config.corridor_bias_strength),
"max_intersections_affected": int(config.max_intersections_affected),
"guidance_persistence_steps": int(config.guidance_persistence_steps),
"guidance_refresh_steps": int(config.guidance_refresh_steps),
"gating_mode": config.gating_mode,
"min_avg_queue_for_guidance": float(config.min_avg_queue_for_guidance),
"min_queue_imbalance_for_guidance": float(config.min_queue_imbalance_for_guidance),
"require_incident_or_spillback": bool(config.require_incident_or_spillback),
"allow_guidance_in_normal_conditions": bool(config.allow_guidance_in_normal_conditions),
"enable_bias_decay": bool(config.enable_bias_decay),
"mean_total_return": distribution_summary(
[safe_float(row.get("total_return")) for row in rows]
)["mean"],
"mean_return_delta_vs_rl_only": distribution_summary(
[safe_float(row.get("total_return_delta_vs_rl_only")) for row in rows]
)["mean"],
"mean_avg_queue_delta_vs_rl_only": distribution_summary(
[safe_float(row.get("avg_queue_delta_vs_rl_only")) for row in rows]
)["mean"],
"mean_avg_wait_delta_vs_rl_only": distribution_summary(
[safe_float(row.get("avg_wait_delta_vs_rl_only")) for row in rows]
)["mean"],
"mean_throughput_delta_vs_rl_only": distribution_summary(
[safe_float(row.get("throughput_delta_vs_rl_only")) for row in rows]
)["mean"],
"mean_travel_time_delta_vs_rl_only": distribution_summary(
[safe_float(row.get("travel_time_delta_vs_rl_only")) for row in rows]
)["mean"],
"mean_percent_steps_with_active_guidance": distribution_summary(
[safe_float(row.get("percent_steps_with_active_guidance")) for row in rows]
)["mean"],
"mean_avg_num_affected_intersections": distribution_summary(
[safe_float(row.get("avg_num_affected_intersections")) for row in rows]
)["mean"],
"mean_avg_num_targeted_intersections": distribution_summary(
[safe_float(row.get("avg_num_targeted_intersections")) for row in rows]
)["mean"],
"mean_num_steps_guidance_blocked_by_gate": distribution_summary(
[safe_float(row.get("num_steps_guidance_blocked_by_gate")) for row in rows]
)["mean"],
"mean_fallback_used_count": distribution_summary(
[safe_float(row.get("fallback_used_count")) for row in rows]
)["mean"],
"mean_invalid_guidance_count": distribution_summary(
[safe_float(row.get("invalid_guidance_count")) for row in rows]
)["mean"],
"num_episodes": int(len(rows)),
}
summary["beats_rl_only"] = bool(summary["mean_return_delta_vs_rl_only"] >= 0.0)
rankings.append(summary)
rankings.sort(
key=lambda item: (
float(item["mean_return_delta_vs_rl_only"]),
float(item["mean_throughput_delta_vs_rl_only"]),
-float(item["mean_avg_queue_delta_vs_rl_only"]),
-float(item["mean_avg_wait_delta_vs_rl_only"]),
),
reverse=True,
)
return rankings
def build_summary_report(
paired_rows: list[dict[str, Any]],
ranking_rows: list[dict[str, Any]],
rl_only_rows: list[dict[str, Any]],
args: argparse.Namespace,
sweep_configs: list[SweepConfigSpec],
) -> dict[str, Any]:
rl_only_mean_total_return = distribution_summary(
[safe_float(row.get("total_return")) for row in rl_only_rows]
)["mean"]
top_5 = ranking_rows[:5]
best_config = ranking_rows[0] if ranking_rows else None
configs_beating_rl_only = [row for row in ranking_rows if row["beats_rl_only"]]
bias_effects = group_rankings_by_parameter(ranking_rows, "bias_strength")
affected_intersections_effects = group_rankings_by_parameter(ranking_rows, "max_intersections_affected")
gating_effects = group_rankings_by_parameter(ranking_rows, "gating_mode")
persistence_effects = group_rankings_by_parameter(ranking_rows, "guidance_persistence_steps")
decay_effects = group_rankings_by_parameter(ranking_rows, "enable_bias_decay")
best_bias = best_group_value(bias_effects)
best_max_affected = best_group_value(affected_intersections_effects)
best_gating = best_group_value(gating_effects)
best_persistence = best_group_value(persistence_effects)
best_decay = best_group_value(decay_effects)
recommendation = None
if best_config is not None:
recommendation = (
f"Start the next paired eval with {best_config['config_id']} "
f"(bias={best_config['bias_strength']}, max_affected={best_config['max_intersections_affected']}, "
f"gate={best_config['gating_mode']}, persistence={best_config['guidance_persistence_steps']}, "
f"decay={best_config['enable_bias_decay']})."
)
return {
"generated_at": datetime.now(timezone.utc).isoformat(),
"preset": args.preset,
"comparison_scope": {
"cities": list(args.cities),
"scenarios": list(args.scenarios),
"seeds": [int(seed) for seed in args.seeds],
"episodes_per_seed": int(args.episodes_per_seed),
"num_sweep_configs": int(len(sweep_configs)),
"num_paired_rows": int(len(paired_rows)),
},
"rl_only_mean_total_return": rl_only_mean_total_return,
"best_overall_config": best_config,
"did_any_rl_llm_config_beat_rl_only": bool(configs_beating_rl_only),
"closest_if_no_beat": None if configs_beating_rl_only else best_config,
"top_5_configs": top_5,
"parameter_effects": {
"bias_strength": bias_effects,
"max_intersections_affected": affected_intersections_effects,
"gating_mode": gating_effects,
"guidance_persistence_steps": persistence_effects,
"enable_bias_decay": decay_effects,
},
"analysis_answers": {
"which_config_was_best_overall": None if best_config is None else best_config["config_id"],
"did_any_rl_llm_config_beat_rl_only": bool(configs_beating_rl_only),
"did_weaker_bias_help": best_bias in {"0.025", "0.05"},
"did_affecting_fewer_intersections_help": best_max_affected == "1",
"did_gating_help": best_gating not in {None, "always_on"},
"did_shorter_persistence_help": best_persistence == "5",
"did_bias_decay_help": best_decay == "True",
},
"recommendation": recommendation,
}
def group_rankings_by_parameter(
ranking_rows: list[dict[str, Any]],
parameter_name: str,
) -> list[dict[str, Any]]:
buckets: dict[str, list[dict[str, Any]]] = {}
for row in ranking_rows:
key = str(row[parameter_name])
buckets.setdefault(key, []).append(row)
grouped: list[dict[str, Any]] = []
for key, rows in sorted(buckets.items(), key=lambda item: item[0]):
grouped.append(
{
"value": key,
"num_configs": int(len(rows)),
"mean_return_delta_vs_rl_only": distribution_summary(
[safe_float(row.get("mean_return_delta_vs_rl_only")) for row in rows]
)["mean"],
"mean_throughput_delta_vs_rl_only": distribution_summary(
[safe_float(row.get("mean_throughput_delta_vs_rl_only")) for row in rows]
)["mean"],
"mean_avg_queue_delta_vs_rl_only": distribution_summary(
[safe_float(row.get("mean_avg_queue_delta_vs_rl_only")) for row in rows]
)["mean"],
"mean_avg_wait_delta_vs_rl_only": distribution_summary(
[safe_float(row.get("mean_avg_wait_delta_vs_rl_only")) for row in rows]
)["mean"],
"mean_percent_steps_with_active_guidance": distribution_summary(
[safe_float(row.get("mean_percent_steps_with_active_guidance")) for row in rows]
)["mean"],
}
)
grouped.sort(
key=lambda item: (
float(item["mean_return_delta_vs_rl_only"]),
float(item["mean_throughput_delta_vs_rl_only"]),
-float(item["mean_avg_queue_delta_vs_rl_only"]),
-float(item["mean_avg_wait_delta_vs_rl_only"]),
),
reverse=True,
)
return grouped
def best_group_value(grouped_rows: list[dict[str, Any]]) -> str | None:
return grouped_rows[0]["value"] if grouped_rows else None
def build_config_payload(
args: argparse.Namespace,
env_config: EnvConfig,
episode_plans: list[Any],
sweep_configs: list[SweepConfigSpec],
) -> dict[str, Any]:
return {
"generated_at": datetime.now(timezone.utc).isoformat(),
"preset": args.preset,
"rl_checkpoint": str(args.rl_checkpoint),
"llm_model_path": str(args.llm_model_path),
"comparison_scope": {
"num_episode_plans": int(len(episode_plans)),
"cities": sorted({plan.city_id for plan in episode_plans}),
"scenarios": sorted({plan.scenario for plan in episode_plans}),
"seeds": sorted({int(plan.seed) for plan in episode_plans}),
"episodes_per_seed": int(args.episodes_per_seed),
"max_episode_seconds": args.max_episode_seconds,
"total_runs": int(len(episode_plans) * (1 + len(sweep_configs))),
},
"episode_plans": [plan.to_dict() for plan in episode_plans],
"sweep_configs": [config.to_dict() for config in sweep_configs],
"influence_configs": {
config.config_id: guidance_config_payload(config.to_influence_config())
for config in sweep_configs
},
"repair_config": asdict(
RepairConfig(
allow_only_visible_candidates=args.allow_only_visible_candidates,
max_target_intersections=args.max_target_intersections,
fallback_on_empty_targets=args.fallback_on_empty_targets,
fallback_mode=args.fallback_mode,
)
),
"env_config": env_config_to_payload(env_config),
"save_step_metrics": bool(args.save_step_metrics),
"save_guidance_traces": bool(args.save_guidance_traces),
}
def format_float_token(value: float) -> str:
text = f"{float(value):.3f}".rstrip("0").rstrip(".")
return text.replace("-", "m").replace(".", "p")
def gating_mode_token(value: str) -> str:
return {
"always_on": "always",
"incident_or_spillback": "incident",
"queue_threshold": "queue",
"imbalance_threshold": "imbalance",
"queue_or_imbalance": "queue_or_imb",
"combined": "combined",
}[value]
if __name__ == "__main__":
main()
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