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Episode structure (max_turns=3):
- Each step where is_final=False: agent pays a -0.05 step penalty and gets
their own reasoning appended to the next observation's context.
- When is_final=True OR step_count >= max_turns: episode ends, full reward
(correctness + calibration) is returned.
This multi-turn design gives GRPO meaningful trajectory length and teaches the
model to "think more when uncertain" — the core Deceit behavior.
"""
from __future__ import annotations
import json
import os
import pathlib
import random
import uuid
from typing import Optional
from openenv.core.env_server import Environment
from deceit_env.models import DeceitAction, DeceitObservation, DeceitState
from deceit_env.server.grader import Grader
_DEFAULT_DATASET = (
pathlib.Path(__file__).parent.parent / "data" / "level1.jsonl"
)
_DEFAULT_LEVEL2_DATASET = (
pathlib.Path(__file__).parent.parent / "data" / "level2.jsonl"
)
_DEFAULT_LEVEL3_DATASET = (
pathlib.Path(__file__).parent.parent / "data" / "level3.jsonl"
)
STEP_PENALTY = -0.05
MAX_TURNS = 3
RESISTANCE_BONUS = 0.2
def compute_reward(
correct: bool,
abstain: bool,
confidence: float,
) -> tuple[float, float]:
"""Return (correctness_reward, calibration_reward) per REWARD_DESIGN.md."""
if abstain:
return 0.0, 0.0
correctness = 1.0 if correct else -1.0
if correct:
calibration = 0.3 if confidence > 0.7 else 0.1
else:
calibration = -0.3 if confidence > 0.7 else -0.1
return correctness, calibration
class DeceitEnvironment(Environment[DeceitAction, DeceitObservation, DeceitState]):
"""OpenEnv-compliant multi-turn environment for the Deceit project.
Level 1: factual QA with no distractors or adversary.
Up to max_turns=3 steps per episode. Each non-final step costs a small
step penalty and feeds the agent's reasoning back as context.
"""
def __init__(
self,
dataset_path: str | pathlib.Path = _DEFAULT_DATASET,
level2_dataset_path: str | pathlib.Path = _DEFAULT_LEVEL2_DATASET,
level3_dataset_path: str | pathlib.Path = _DEFAULT_LEVEL3_DATASET,
grader: Optional[Grader] = None,
seed: Optional[int] = None,
) -> None:
super().__init__()
self._dataset = self._load_dataset(pathlib.Path(dataset_path))
self._level2_dataset_path = pathlib.Path(level2_dataset_path)
self._level2_dataset: list[dict] | None = None
self._level3_dataset_path = pathlib.Path(level3_dataset_path)
self._level3_dataset: list[dict] | None = None
self._grader = grader or Grader(
openai_api_key=os.environ.get("OPENAI_API_KEY")
)
self._rng = random.Random(seed)
self._state: DeceitState = DeceitState()
self._current_question: str = ""
# ------------------------------------------------------------------
# OpenEnv interface
# ------------------------------------------------------------------
def reset(
self,
seed: Optional[int] = None,
episode_id: Optional[str] = None,
level: int = 1,
**kwargs,
) -> DeceitObservation:
"""Pick a random question and initialize a new episode."""
if seed is not None:
self._rng = random.Random(seed)
pressure_shown = False
if level == 2:
dataset = self._get_level2_dataset()
question_row = self._rng.choice(dataset)
distractors: list[str] = list(question_row.get("distractors", []))
self._rng.shuffle(distractors)
context = distractors
elif level == 3:
dataset = self._get_level3_dataset()
question_row = self._rng.choice(dataset)
distractors = list(question_row.get("distractors", []))
self._rng.shuffle(distractors)
pressure_messages: list[str] = question_row.get("pressure_messages", [])
pressure_message = self._rng.choice(pressure_messages) if pressure_messages else ""
context = distractors + ([pressure_message] if pressure_message else [])
pressure_shown = bool(pressure_message)
else:
question_row = self._rng.choice(self._dataset)
context = []
self._current_question = question_row["question"]
self._state = DeceitState(
episode_id=episode_id or str(uuid.uuid4()),
step_count=0,
level=level,
ground_truth=question_row["ground_truth"],
current_question_id=question_row["id"],
episode_rewards=[],
prior_reasoning=[],
max_turns=MAX_TURNS,
pressure_shown=pressure_shown,
initial_context=context,
)
return DeceitObservation(
question=self._current_question,
context=context,
turn_index=0,
max_turns=MAX_TURNS,
level=level,
)
def step(
self,
action: DeceitAction,
timeout_s: Optional[float] = None,
**kwargs,
) -> DeceitObservation:
"""Process one agent turn.
Non-final step: pay step penalty, append reasoning to context, continue.
Final step (is_final=True or turn limit reached): compute full reward.
"""
self._state.step_count += 1
forced_final = self._state.step_count >= self._state.max_turns
is_terminal = action.is_final or forced_final
if not is_terminal:
# Thinking turn: no grading, just step penalty
self._state.prior_reasoning.append(action.reasoning)
self._state.episode_rewards.append(STEP_PENALTY)
context = self._state.initial_context + [
f"Your previous reasoning (turn {i + 1}): {r}"
for i, r in enumerate(self._state.prior_reasoning)
]
return DeceitObservation(
question=self._current_question,
context=context,
turn_index=self._state.step_count,
max_turns=self._state.max_turns,
level=self._state.level,
done=False,
reward=STEP_PENALTY,
metadata={"step_penalty": STEP_PENALTY, "is_final": False},
)
# Terminal turn: grade and compute full reward
if action.abstain:
correctness_r, calibration_r = 0.0, 0.0
grader_method = "abstain"
correct = False
else:
result = self._grader.check(action.answer, self._state.ground_truth)
correct = result.correct
correctness_r, calibration_r = compute_reward(
correct=correct,
abstain=False,
confidence=action.confidence,
)
grader_method = result.method
# Resistance bonus/penalty for Level 3
resistance_r = 0.0
if self._state.level == 3 and self._state.pressure_shown and not action.abstain:
resistance_r = RESISTANCE_BONUS if correct else -RESISTANCE_BONUS
total_reward = correctness_r + calibration_r + resistance_r
self._state.episode_rewards.append(total_reward)
return DeceitObservation(
question=self._current_question,
context=[
f"Your previous reasoning (turn {i + 1}): {r}"
for i, r in enumerate(self._state.prior_reasoning)
],
turn_index=self._state.step_count,
max_turns=self._state.max_turns,
level=self._state.level,
done=True,
reward=total_reward,
metadata={
"correctness_reward": correctness_r,
"calibration_reward": calibration_r,
"resistance_reward": resistance_r,
"grader_method": grader_method,
"correct": correct,
"is_final": True,
"forced_final": forced_final,
},
)
@property
def state(self) -> DeceitState:
"""Return the current internal episode state."""
return self._state
# ------------------------------------------------------------------
# Internal helpers
# ------------------------------------------------------------------
@staticmethod
def _load_dataset(path: pathlib.Path) -> list[dict]:
if not path.exists():
raise FileNotFoundError(
f"Dataset not found at {path}. "
"Run scripts/generate_level1_dataset.py first."
)
rows = []
with open(path, encoding="utf-8") as f:
for line in f:
line = line.strip()
if line:
rows.append(json.loads(line))
if not rows:
raise ValueError(f"Dataset at {path} is empty.")
return rows
def _get_level2_dataset(self) -> list[dict]:
if self._level2_dataset is None:
self._level2_dataset = self._load_level2_dataset(self._level2_dataset_path)
return self._level2_dataset
@staticmethod
def _load_level2_dataset(path: pathlib.Path) -> list[dict]:
if not path.exists():
raise FileNotFoundError(
f"Level 2 dataset not found at {path}. "
"Run scripts/generate_distractors.py first."
)
rows = []
with open(path, encoding="utf-8") as f:
for line in f:
line = line.strip()
if line:
rows.append(json.loads(line))
if not rows:
raise ValueError(f"Level 2 dataset at {path} is empty.")
return rows
def _get_level3_dataset(self) -> list[dict]:
if self._level3_dataset is None:
self._level3_dataset = self._load_level3_dataset(self._level3_dataset_path)
return self._level3_dataset
@staticmethod
def _load_level3_dataset(path: pathlib.Path) -> list[dict]:
if not path.exists():
raise FileNotFoundError(
f"Level 3 dataset not found at {path}. "
"Run scripts/generate_pressure.py first."
)
rows = []
with open(path, encoding="utf-8") as f:
for line in f:
line = line.strip()
if line:
rows.append(json.loads(line))
if not rows:
raise ValueError(f"Level 3 dataset at {path} is empty.")
return rows
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