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openenv_wrapper.py — OpenEnv-compatible environment wrapping StudentSimulator.
"""
from __future__ import annotations
import re
from dataclasses import dataclass, field
from typing import Any, Optional
try:
import openenv # type: ignore
if not hasattr(openenv, 'Environment'):
raise ImportError
_OPENENV_AVAILABLE = True
except ImportError:
_OPENENV_AVAILABLE = False
class _EnvBase:
def reset(self): raise NotImplementedError
def step(self, action): raise NotImplementedError
openenv = type("openenv", (), {"Environment": _EnvBase})()
from .student_fsm import (
MisconceptionType, StudentType, StudentSimulator,
TutorAction, StudentState, encode_state,
)
# ---------------------------------------------------------------------------
# Observation
# ---------------------------------------------------------------------------
@dataclass
class Observation:
student_response: str
confusion: float
attention: float
learning_trend: float
turn: int
misconception_id: MisconceptionType
student_type: StudentType
last_action: Optional[TutorAction] = None
steps_taken: int = 0
recent_actions: tuple[str, ...] = field(default_factory=tuple)
def to_dict(self) -> dict[str, Any]:
return {
"student_response": self.student_response,
"confusion": self.confusion,
"attention": self.attention,
"learning_trend": self.learning_trend,
"turn": self.turn,
"misconception_id": self.misconception_id.value,
"student_type": self.student_type.value,
"last_action": self.last_action.value if self.last_action else None,
"steps_taken": self.steps_taken,
"recent_actions": list(self.recent_actions),
}
def to_numpy(self):
from .student_fsm import StudentState, encode_state
snap = StudentState(
misconception_id = self.misconception_id,
student_type = self.student_type,
confusion = self.confusion,
attention = self.attention,
learning_trend = self.learning_trend,
turn = self.turn,
last_action = self.last_action,
)
return encode_state(snap)
# ---------------------------------------------------------------------------
# StepInfo
# ---------------------------------------------------------------------------
@dataclass
class StepInfo:
done_reason: Optional[str]
raw_action: str
parsed_action: Optional[TutorAction]
misconception_id: MisconceptionType
# ---------------------------------------------------------------------------
# Strategy tag parser
# ---------------------------------------------------------------------------
_STRATEGY_PATTERN = re.compile(
r"<STRATEGY>\s*([a-z_]+)\s*</STRATEGY>",
re.IGNORECASE,
)
def _parse_action(action_str: str) -> Optional[TutorAction]:
match = _STRATEGY_PATTERN.search(action_str)
if not match:
return None
try:
return TutorAction(match.group(1).lower())
except ValueError:
return None
# ---------------------------------------------------------------------------
# Teaching phase shaping
# ---------------------------------------------------------------------------
_PHASE_BONUS = 0.2 # small — guide without dominating
_PHASE_PREFERRED: dict[int, set[TutorAction]] = {
0: {TutorAction.QUESTION, TutorAction.EXPLAIN}, # diagnose (confusion > 6)
1: {TutorAction.WORKED_EXAMPLE, TutorAction.ANALOGIZE}, # intervene (confusion > 3.5)
2: {TutorAction.QUESTION, TutorAction.CORRECT_FACT}, # consolidate (confusion <= 3.5)
}
def _phase_index(confusion: float) -> int:
if confusion > 6.0: return 0
if confusion > 3.5: return 1
return 2
# ---------------------------------------------------------------------------
# Episode constants
# ---------------------------------------------------------------------------
MAX_TURNS = 15
CONFUSION_SUCCESS = 2.0
ATTENTION_FLOOR = 0.5 # Lowered — soft penalty only above, terminal only at near-zero
# ---------------------------------------------------------------------------
# EduForgeEnv
# ---------------------------------------------------------------------------
class EduForgeEnv(openenv.Environment):
"""
OpenEnv-compatible tutoring environment.
Done conditions
---------------
success : confusion <= 2.0
timeout : turn_count >= 15
disengaged : attention <= 0.5 (near-zero only — no premature kills)
"""
def __init__(
self,
seed: Optional[int] = None,
confusion_init: Optional[float] = None,
attention_init: Optional[float] = None,
misconception_init: Optional[str] = None,
) -> None:
self._seed = seed
self._confusion_init = confusion_init
self._attention_init = attention_init
self._misconception_init = misconception_init
self._sim: Optional[StudentSimulator] = None
from ..rewards.engine import RewardEngine
self._reward_engine = RewardEngine()
self._turn_count: int = 0
self._recent_actions: list[TutorAction] = []
# ------------------------------------------------------------------
# reset
# ------------------------------------------------------------------
def reset(self) -> Observation:
m_init: Optional[MisconceptionType] = None
if self._misconception_init is not None:
try:
m_init = MisconceptionType(self._misconception_init)
except ValueError:
pass
self._sim = StudentSimulator(
seed = self._seed,
confusion_init = self._confusion_init,
attention_init = self._attention_init,
misconception_init = m_init,
)
self._turn_count = 0
self._recent_actions = []
self._reward_engine.reset()
snap = self._sim.state_snapshot()
initial_response = self._sim.generate_response()
return Observation(
student_response = initial_response,
confusion = snap.confusion,
attention = snap.attention,
learning_trend = snap.learning_trend,
turn = self._turn_count,
misconception_id = snap.misconception_id,
student_type = snap.student_type,
last_action = None,
steps_taken = 0,
recent_actions = tuple(),
)
# ------------------------------------------------------------------
# step
# ------------------------------------------------------------------
def step(
self, action_str: str
) -> tuple[Observation, float, bool, StepInfo]:
if self._sim is None:
raise RuntimeError("Call reset() before step().")
# 1. Parse
parsed_action = _parse_action(action_str)
format_valid = parsed_action is not None
if parsed_action is None:
parsed_action = TutorAction.REPEAT
# 2. Pre-transition snapshot
confusion_before = self._sim.confusion
attention_before = self._sim.attention
misconception_now = self._sim.misconception_id
# 3. Phase bonus
phase = _phase_index(confusion_before)
phase_bonus = _PHASE_BONUS if parsed_action in _PHASE_PREFERRED[phase] else 0.0
# 4. Transition
self._sim.transition(parsed_action)
self._turn_count += 1
snap = self._sim.state_snapshot()
# 5. Done conditions
done = False
done_reason: Optional[str] = None
if snap.confusion <= CONFUSION_SUCCESS:
done = True
done_reason = "success"
elif snap.attention <= ATTENTION_FLOOR:
done = True
done_reason = "disengaged"
elif self._turn_count >= MAX_TURNS:
done = True
done_reason = "timeout"
# 6. Reward
reward, _components = self._reward_engine.compute(
confusion_before = confusion_before,
confusion_after = snap.confusion,
attention_before = attention_before,
attention_after = snap.attention,
action_text = action_str,
format_valid = format_valid,
learning_trend = snap.learning_trend,
action = parsed_action,
action_history = [a.value for a in self._recent_actions[-3:]],
misconception = misconception_now,
done = done,
done_reason = done_reason,
phase_bonus = phase_bonus,
episode_length = self._turn_count,
)
# 7. Update history
self._recent_actions.append(parsed_action)
if len(self._recent_actions) > 5:
self._recent_actions.pop(0)
obs = Observation(
student_response = snap.last_response,
confusion = snap.confusion,
attention = snap.attention,
learning_trend = snap.learning_trend,
turn = self._turn_count,
misconception_id = snap.misconception_id,
student_type = snap.student_type,
last_action = snap.last_action,
steps_taken = self._turn_count,
recent_actions = tuple(a.value for a in self._recent_actions[-3:]),
)
info = StepInfo(
done_reason = done_reason,
raw_action = action_str,
parsed_action = parsed_action,
misconception_id = snap.misconception_id,
)
return obs, reward, done, info
# ------------------------------------------------------------------
# Helpers
# ------------------------------------------------------------------
@property
def turn_count(self) -> int:
return self._turn_count
def __repr__(self) -> str:
if self._sim is None:
return "EduForgeEnv(not started)"
snap = self._sim.state_snapshot()
return (
f"EduForgeEnv(turn={self._turn_count}, "
f"confusion={snap.confusion:.2f}, "
f"attention={snap.attention:.2f}, "
f"misconception={snap.misconception_id.value})"
) |