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import json
import random
import re
from pathlib import Path
from typing import Any, Iterable
from environment import SentinelEnv
from mission_context import build_orchestrator_prompt
from sentinel_config import ADVERSARIAL_AWARENESS_STAKES
ACTION_RE = re.compile(r"\{.*\}", re.DOTALL)
def load_replay(path: str | Path) -> dict[tuple[str, int, int], dict[str, Any]]:
"""Load trained action replay keyed by (task_type, seed, step_count)."""
table: dict[tuple[str, int, int], dict[str, Any]] = {}
replay_path = Path(path)
if not replay_path.exists():
return table
for line in replay_path.read_text().splitlines():
if not line.strip():
continue
row = json.loads(line)
key = (str(row["task_type"]), int(row["seed"]), int(row["step"]))
table[key] = dict(row["action"])
return table
class TrainedReplayPolicy:
"""
Policy callable for training/evaluate.py.
The Space does not need a GPU at runtime. It looks up a recorded action for
the current task, seed, and step. Missing rows fall back to the heuristic so
demos remain robust for unseen seeds.
"""
def __init__(self, replay_path: str | Path) -> None:
self.replay_path = Path(replay_path)
self._table = load_replay(self.replay_path)
self._task_type = "task3"
self._seed = 0
def set_episode(self, task_type: str, seed: int) -> None:
self._task_type = task_type
self._seed = seed
def __call__(self, env: SentinelEnv, obs: dict, rng: random.Random) -> dict:
key = (self._task_type, self._seed, int(obs.get("step_count", 0)))
action = dict(self._table.get(key) or {})
if not action:
action = heuristic_action(obs)
action["reasoning"] = "trained replay miss; heuristic fallback"
action["replay_miss"] = True
action["session_id"] = obs["session_id"]
action["task_type"] = obs["task_type"]
return sanitize_action(action, obs)
def replay_trained_policy(replay_path: str | Path) -> TrainedReplayPolicy:
return TrainedReplayPolicy(replay_path)
def record_trained_actions(
adapter_path: str | Path,
base_model: str,
tasks: Iterable[str],
seeds: Iterable[int],
out_path: str | Path = "outputs/trained_policy_replay.jsonl",
max_new_tokens: int = 192,
) -> Path:
"""
Roll out a trained LoRA policy and write replay JSONL.
In Colab, this loads the trained adapter and samples model actions. Locally,
if training dependencies or adapter files are unavailable, it falls back to
the heuristic policy and marks rows with model_source="heuristic_fallback".
"""
out = Path(out_path)
out.parent.mkdir(parents=True, exist_ok=True)
generator = _load_generator(adapter_path, base_model, max_new_tokens)
rows: list[dict[str, Any]] = []
for task_type in tasks:
for seed in seeds:
env = SentinelEnv()
result = env.reset(task_type=task_type, seed=int(seed))
while not result["done"]:
obs = result["observation"]
if generator is None:
action = heuristic_action(obs)
model_source = "heuristic_fallback"
else:
text = generator(build_orchestrator_prompt(obs))
action = parse_action(text, obs)
model_source = "trained_lora"
action["reasoning"] = action.get("reasoning") or model_source
rows.append(
{
"task_type": task_type,
"seed": int(seed),
"scenario_id": obs.get("scenario_id"),
"step": int(obs.get("step_count", 0)),
"action": {
key: value
for key, value in action.items()
if key in {"action_type", "specialist_id", "subtask_response", "reasoning"}
},
"model_source": model_source,
}
)
result = env.step(action)
with out.open("w") as handle:
for row in rows:
handle.write(json.dumps(row, sort_keys=True) + "\n")
return out
def _load_generator(adapter_path: str | Path, base_model: str, max_new_tokens: int):
adapter = Path(adapter_path)
if not adapter.exists():
return None
try:
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
except Exception:
return None
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(
base_model,
device_map="auto",
quantization_config=quantization_config,
)
model = PeftModel.from_pretrained(model, str(adapter))
model.eval()
def generate(prompt: str) -> str:
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
output = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
)
return tokenizer.decode(output[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
return generate
def parse_action(text: str, obs: dict) -> dict[str, Any]:
match = ACTION_RE.search(text or "")
payload: dict[str, Any] = {}
if match:
try:
payload = json.loads(match.group(0))
except json.JSONDecodeError:
payload = {}
return sanitize_action(payload, obs)
def sanitize_action(payload: dict[str, Any], obs: dict) -> dict[str, Any]:
action_type = payload.get("action_type", "delegate")
if action_type not in {"delegate", "verify", "solve_independently", "skip"}:
action_type = "delegate"
specialist_id = payload.get("specialist_id")
if action_type in {"delegate", "verify"} and specialist_id not in obs["available_specialists"]:
specialist_id = max(
obs["available_specialists"],
key=lambda sid: obs["trust_snapshot"].get(sid, 0.5),
)
if action_type in {"solve_independently", "skip"}:
specialist_id = None
return {
"session_id": obs["session_id"],
"task_type": obs["task_type"],
"action_type": action_type,
"specialist_id": specialist_id,
"subtask_response": "SELF_SOLVED" if action_type == "solve_independently" else None,
"reasoning": payload.get("reasoning", "trained replay action"),
}
def heuristic_action(obs: dict) -> dict[str, Any]:
trust = obs["trust_snapshot"]
specialist = max(obs["available_specialists"], key=lambda sid: trust.get(sid, 0.5))
action_type = (
"verify"
if obs["stakes_level"] >= ADVERSARIAL_AWARENESS_STAKES and trust.get(specialist, 0.5) < 0.70
else "delegate"
)
return {
"session_id": obs["session_id"],
"task_type": obs["task_type"],
"action_type": action_type,
"specialist_id": specialist,
"subtask_response": None,
"reasoning": "heuristic replay baseline",
}
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