rename: objectives -> additional_objectives in NuconGoalEnv

Clarifies that the goal reward is the primary built-in objective;
additional_objectives are additive on top of it.

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

README.md

@@ -214,7 +214,7 @@ env = NuconGoalEnv(
     # Keep policy within the simulator's known data distribution.
     # SIM_UNCERTAINTY (kNN-GP posterior std) is injected into obs when a simulator is active.
     # Tune start/scale/threshold to taste.
-    objectives=[UncertaintyPenalty(start=0.3, scale=1.0)],   # L2 penalty above soft threshold
+    additional_objectives=[UncertaintyPenalty(start=0.3, scale=1.0)],  # L2 penalty above soft threshold
     terminators=[UncertaintyAbort(threshold=0.7)],            # abort episode at hard threshold
 )
 # Or use a preset: env = gym.make('Nucon-goal_power-v0', simulator=simulator)

nucon/rl.py

@@ -280,8 +280,8 @@ class NuconGoalEnv(gym.Env):
         seconds_per_step=5,
         terminators=None,
         terminate_above=0,
-        objectives=None,
-        objective_weights=None,
+        additional_objectives=None,
+        additional_objective_weights=None,
     ):
         super().__init__()
 
@@ -351,9 +351,9 @@ class NuconGoalEnv(gym.Env):
         self.action_space = spaces.Dict(action_spaces)
 
         self._terminators = terminators or []
-        _objs = objectives or []
+        _objs = additional_objectives or []
         self._objectives = [Objectives[o] if isinstance(o, str) else o for o in _objs]
-        self._objective_weights = objective_weights or [1.0] * len(self._objectives)
+        self._objective_weights = additional_objective_weights or [1.0] * len(self._objectives)
         self._desired_goal = np.zeros(n_goals, dtype=np.float32)
         self._total_steps = 0