diff --git a/README.md b/README.md
index 9a17244..f955c52 100644
--- a/README.md
+++ b/README.md
@@ -196,7 +196,7 @@ env.close()
 HER works by relabelling past trajectories with the goal that was *actually achieved*, turning every episode into useful training signal even when the agent never reaches the intended target. This makes it much more sample-efficient than standard RL for goal-reaching tasks. This matters a lot given how slow the real game is.
 
 ```python
-from nucon.rl import NuconGoalEnv, UncertaintyPenalty, UncertaintyAbort
+from nucon.rl import NuconGoalEnv, Parameterized_Objectives, Parameterized_Terminators
 from stable_baselines3 import SAC
 from stable_baselines3.common.buffers import HerReplayBuffer
 
@@ -214,8 +214,8 @@ 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.
-    additional_objectives=[UncertaintyPenalty(start=0.3, scale=1.0)],  # L2 penalty above soft threshold
-    terminators=[UncertaintyAbort(threshold=0.7)],            # abort episode at hard threshold
+    additional_objectives=[Parameterized_Objectives['uncertainty_penalty'](start=0.3, scale=1.0)],
+    terminators=[Parameterized_Terminators['uncertainty_abort'](threshold=0.7)],
 )
 # Or use a preset: env = gym.make('Nucon-goal_power-v0', simulator=simulator)
 
diff --git a/nucon/rl.py b/nucon/rl.py
index 93a4509..2fbf22d 100644
--- a/nucon/rl.py
+++ b/nucon/rl.py
@@ -43,9 +43,6 @@ Parameterized_Terminators = {
     "uncertainty_abort": _uncertainty_abort,  # (threshold,) -> (obs) -> float
 }
 
-# Convenience aliases
-UncertaintyPenalty = _uncertainty_penalty
-UncertaintyAbort   = _uncertainty_abort
 
 
 # ---------------------------------------------------------------------------