fix bug in state switching env

neurolib/control/reinforcement_learning/environments/state_switching.py

@@ -105,7 +105,7 @@ def reset(self, seed=None, options=None):
     def _loss(self, obs, action):
         control_loss = abs(self.targetstate[0] - obs["exc"].item()) + abs(self.targetstate[1] - obs["inh"].item())
         control_strength_loss = np.abs(action).sum() * self.l1_control_strength_loss_scale
-        control_strength_loss += np.sqrt(np.sum(action**2)) * self.l2_control_strength_loss_scale
+        control_strength_loss += np.sqrt(np.sum(np.square(action))) * self.l2_control_strength_loss_scale
         return control_loss + control_strength_loss
 
     def step(self, action):