refactor: move UncertaintyPenalty/Abort into Parameterized_Objectives/Terminators dicts

- uncertainty_penalty -> Parameterized_Objectives['uncertainty_penalty']
- uncertainty_abort   -> Parameterized_Terminators['uncertainty_abort']
- Add Parameterized_Terminators dict (same pattern as Parameterized_Objectives)
- Keep UncertaintyPenalty / UncertaintyAbort as convenience aliases

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

nucon/rl.py

@@ -18,35 +18,7 @@ Objectives = {
     "episode_time": lambda obs: obs["EPISODE_TIME"],
 }
 
-Parameterized_Objectives = {
+def _uncertainty_penalty(start=0.3, scale=1.0, mode='l2'):
-    "target_temperature": lambda goal_temp: lambda obs: -((obs["CORE_TEMP"] - goal_temp) ** 2),
-    "target_gap":         lambda goal_gap:  lambda obs: -((obs["CORE_TEMP"] - obs["CORE_TEMP_MIN"] - goal_gap) ** 2),
-    "temp_below":         lambda max_temp:  lambda obs: -(np.clip(obs["CORE_TEMP"] - max_temp,  0, np.inf) ** 2),
-    "temp_above":         lambda min_temp:  lambda obs: -(np.clip(min_temp - obs["CORE_TEMP"],  0, np.inf) ** 2),
-    "constant":           lambda constant:  lambda obs: constant,
-}
-
-
-def UncertaintyPenalty(start: float = 0.3, scale: float = 1.0, mode: str = 'l2') -> Callable:
-    """Objective that penalises high simulator uncertainty.
-
-    Returns a callable ``(obs) -> float`` suitable for use as an objective or
-    terminator in NuconEnv / NuconGoalEnv.  Works because ``SIM_UNCERTAINTY``
-    is injected into the obs dict whenever a simulator is active.
-
-    Args:
-        start: uncertainty level at which the penalty starts (default 0.3).
-        scale: penalty coefficient.
-        mode:  ``'l2'`` (quadratic, default) or ``'linear'``.
-
-    Example::
-
-        env = NuconEnv(
-            objectives=['max_power', UncertaintyPenalty(start=0.3, scale=2.0)],
-            objective_weights=[1.0, 1.0],
-            simulator=simulator,
-        )
-    """
     excess = lambda obs: max(0.0, obs.get('SIM_UNCERTAINTY', 0.0) - start)
     if mode == 'l2':
         return lambda obs: -scale * excess(obs) ** 2
@@ -55,25 +27,26 @@ def UncertaintyPenalty(start: float = 0.3, scale: float = 1.0, mode: str = 'l2')
     else:
         raise ValueError(f"Unknown mode '{mode}'. Use 'l2' or 'linear'.")
 
-
+def _uncertainty_abort(threshold=0.7):
-def UncertaintyAbort(threshold: float = 0.7) -> Callable:
-    """Terminator that aborts the episode when simulator uncertainty is too high.
-
-    Returns a callable ``(obs) -> float`` for use as a *terminator*.  When
-    the GP posterior std exceeds ``threshold`` the episode is truncated
-    (``terminated=True``).
-
-    Example::
-
-        env = NuconEnv(
-            objectives=['max_power'],
-            terminators=[UncertaintyAbort(threshold=0.7)],
-            terminate_above=0,
-            simulator=simulator,
-        )
-    """
     return lambda obs: 1.0 if obs.get('SIM_UNCERTAINTY', 0.0) >= threshold else 0.0
 
+Parameterized_Objectives = {
+    "target_temperature":  lambda goal_temp: lambda obs: -((obs["CORE_TEMP"] - goal_temp) ** 2),
+    "target_gap":          lambda goal_gap:  lambda obs: -((obs["CORE_TEMP"] - obs["CORE_TEMP_MIN"] - goal_gap) ** 2),
+    "temp_below":          lambda max_temp:  lambda obs: -(np.clip(obs["CORE_TEMP"] - max_temp,  0, np.inf) ** 2),
+    "temp_above":          lambda min_temp:  lambda obs: -(np.clip(min_temp - obs["CORE_TEMP"],  0, np.inf) ** 2),
+    "constant":            lambda constant:  lambda obs: constant,
+    "uncertainty_penalty": _uncertainty_penalty,  # (start, scale, mode) -> (obs) -> float
+}
+
+Parameterized_Terminators = {
+    "uncertainty_abort": _uncertainty_abort,  # (threshold,) -> (obs) -> float
+}
+
+# Convenience aliases
+UncertaintyPenalty = _uncertainty_penalty
+UncertaintyAbort   = _uncertainty_abort
+
 
 # ---------------------------------------------------------------------------
 # Internal helpers