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Deterministic evaluator: runs a trained model on tasks and returns grader scores.
Usage:
python -m rl.evaluate --model results/best_model/phase2_final.zip --episodes 3
python -m rl.evaluate --model results/best_model/phase3_final.zip --episodes 3 --model-type recurrent
"""
from __future__ import annotations
import argparse
import json
from dataclasses import dataclass, asdict
from typing import Any, Literal
import numpy as np
from sb3_contrib import MaskablePPO, RecurrentPPO
from sb3_contrib.common.maskable.utils import get_action_masks
from rl.gov_workflow_env import GovWorkflowGymEnv
from app.graders import grade_episode
from app.tasks import TASKS
TASK_IDS = [
"district_backlog_easy",
"mixed_urgency_medium",
"cross_department_hard",
]
ModelType = Literal["auto", "maskable", "recurrent"]
@dataclass
class TaskEvalResult:
task_id: str
seed: int
grader_score: float
total_reward: float
total_steps: int
total_completed: int
total_sla_breaches: int
fairness_gap: float
def _normalize_action(action: Any) -> int:
if isinstance(action, np.ndarray):
return int(action.item())
return int(action)
def _apply_eval_action_mask(action_idx: int, masks: np.ndarray) -> int:
if 0 <= action_idx < masks.shape[0] and bool(masks[action_idx]):
return action_idx
if masks.shape[0] > 18 and bool(masks[18]):
return 18
valid = np.flatnonzero(masks)
if valid.size == 0:
return 18
return int(valid[0])
def predict_recurrent_action(
model: Any,
obs: np.ndarray,
lstm_state: Any,
episode_start: np.ndarray,
masks: np.ndarray,
) -> tuple[int, Any]:
action, next_state = model.predict(
obs,
state=lstm_state,
episode_start=episode_start,
deterministic=True,
)
action_idx = _normalize_action(action)
action_idx = _apply_eval_action_mask(action_idx, masks)
return action_idx, next_state
def _load_model(model_path: str, model_type: ModelType) -> tuple[Any, str]:
if model_type == "maskable":
try:
return MaskablePPO.load(model_path), "maskable"
except Exception as exc:
raise ValueError(
"Failed to load as MaskablePPO. This checkpoint may be recurrent. "
"Try: --model-type recurrent"
) from exc
if model_type == "recurrent":
try:
return RecurrentPPO.load(model_path), "recurrent"
except Exception as exc:
raise ValueError(
"Failed to load as RecurrentPPO. This checkpoint may be maskable. "
"Try: --model-type maskable"
) from exc
try:
return MaskablePPO.load(model_path), "maskable"
except Exception:
return RecurrentPPO.load(model_path), "recurrent"
def evaluate_model(
model_path: str,
task_ids: list[str] = TASK_IDS,
n_episodes: int = 1,
verbose: bool = True,
model_type: ModelType = "auto",
) -> list[TaskEvalResult]:
model, resolved_type = _load_model(model_path, model_type)
results = []
for task_id in task_ids:
task_cfg = TASKS.get(task_id)
if task_cfg is None:
print(f"[Eval] Task {task_id!r} not found, skipping.")
continue
ep_rewards, ep_scores = [], []
last_info: dict[str, Any] = {}
for ep in range(n_episodes):
env = GovWorkflowGymEnv(task_id=task_id, seed=task_cfg.seed + ep)
obs, _ = env.reset()
done, ep_reward = False, 0.0
if resolved_type == "recurrent":
lstm_state: Any = None
episode_start = np.array([True], dtype=bool)
while not done:
masks = env.action_masks()
action_idx, lstm_state = predict_recurrent_action(
model=model,
obs=obs,
lstm_state=lstm_state,
episode_start=episode_start,
masks=masks,
)
obs, reward, terminated, truncated, info = env.step(action_idx)
ep_reward += reward
done = terminated or truncated
episode_start = np.array([done], dtype=bool)
last_info = info
else:
while not done:
masks = get_action_masks(env)
action, _ = model.predict(obs, action_masks=masks, deterministic=True)
obs, reward, terminated, truncated, info = env.step(int(action))
ep_reward += reward
done = terminated or truncated
last_info = info
gr = grade_episode(env._core_env.state())
ep_rewards.append(ep_reward)
ep_scores.append(gr.score)
ep_state = env._core_env.state()
result = TaskEvalResult(
task_id=task_id,
seed=task_cfg.seed,
grader_score=float(np.mean(ep_scores)),
total_reward=float(np.mean(ep_rewards)),
total_steps=ep_state.total_steps,
total_completed=ep_state.total_completed,
total_sla_breaches=ep_state.total_sla_breaches,
fairness_gap=float(last_info.get("fairness_gap", 0.0)),
)
results.append(result)
if verbose:
print(
f"[Eval] {task_id:<30} "
f"score={result.grader_score:.4f} "
f"reward={result.total_reward:.2f} "
f"completed={result.total_completed} "
f"sla_breaches={result.total_sla_breaches}"
)
return results
def compare_recurrent_vs_flat(
flat_model_path: str,
recurrent_model_path: str,
task_id: str = "mixed_urgency_medium",
n_episodes: int = 3,
) -> dict[str, float]:
flat = evaluate_model(
flat_model_path,
task_ids=[task_id],
n_episodes=n_episodes,
verbose=False,
model_type="maskable",
)[0].grader_score
recurrent = evaluate_model(
recurrent_model_path,
task_ids=[task_id],
n_episodes=n_episodes,
verbose=False,
model_type="recurrent",
)[0].grader_score
return {
"flat": float(flat),
"recurrent": float(recurrent),
"delta": float(recurrent - flat),
}
def main() -> None:
parser = argparse.ArgumentParser(description="Evaluate a trained PPO model")
parser.add_argument("--model", required=True)
parser.add_argument(
"--task",
default=None,
choices=TASK_IDS,
help="Single-task alias. If set, overrides --tasks.",
)
parser.add_argument("--tasks", nargs="+", default=TASK_IDS)
parser.add_argument("--episodes", type=int, default=1)
parser.add_argument("--output", default=None)
parser.add_argument(
"--model-type",
choices=["auto", "maskable", "recurrent"],
default="auto",
help="Model class to load. Use auto for best-effort detection.",
)
args = parser.parse_args()
selected_tasks = [args.task] if args.task else args.tasks
results = evaluate_model(
args.model,
task_ids=selected_tasks,
n_episodes=args.episodes,
model_type=args.model_type,
)
if args.output:
import os
os.makedirs(os.path.dirname(args.output) or ".", exist_ok=True)
with open(args.output, "w", encoding="utf-8") as f:
json.dump([asdict(r) for r in results], f, indent=2)
print(f"\n[Eval] Results saved to {args.output}")
avg = np.mean([r.grader_score for r in results])
print(f"\n[Eval] Average grader score: {avg:.4f}")
if __name__ == "__main__":
main()
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