only create ShieldHandler when necessary

this also renames camelcase variable logDir

examples/shields/rl/13_minigridsb.py

@@ -29,29 +29,28 @@ def main():
     formula = args.formula
     shield_value = args.shield_value
     shield_comparison = args.shield_comparison
-    logDir = create_log_dir(args)
+    log_dir = create_log_dir(args)
 
-    shield_handler = MiniGridShieldHandler(GRID_TO_PRISM_BINARY, args.grid_file, args.prism_output_file, formula, shield_value=shield_value, shield_comparison=shield_comparison, cleanup=args.cleanup)
     env = gym.make(args.env, render_mode="rgb_array")
     env = RGBImgObsWrapper(env)
     env = ImgObsWrapper(env)
     env = MiniWrapper(env)
     if args.shielding == ShieldingConfig.Full or args.shielding == ShieldingConfig.Training:
+        shield_handler = MiniGridShieldHandler(GRID_TO_PRISM_BINARY, args.grid_file, args.prism_output_file, formula, shield_value=shield_value, shield_comparison=shield_comparison, cleanup=args.cleanup)
         env = MiniGridSbShieldingWrapper(env, shield_handler=shield_handler, create_shield_at_reset=False)
         env = ActionMasker(env, mask_fn)
     else:
         env = ActionMasker(env, nomask_fn)
-    model = MaskablePPO("CnnPolicy", env, verbose=1, tensorboard_log=logDir, device="auto")
+    model = MaskablePPO("CnnPolicy", env, verbose=1, tensorboard_log=log_dir, device="auto")
 
 
-    evalCallback = EvalCallback(env, best_model_save_path=logDir,
-                                log_path=logDir, eval_freq=max(500,  int(args.steps/30)),
+    evalCallback = EvalCallback(env, best_model_save_path=log_dir,
+                                log_path=log_dir, eval_freq=max(500,  int(args.steps/30)),
                                 deterministic=True, render=False)
     steps = args.steps
 
     model.learn(steps,callback=[ImageRecorderCallback(), InfoCallback()])
 
-
     #vec_env = model.get_env()
     #obs = vec_env.reset()
     #terminated = truncated = False