diff --git a/mbpo/optimizers/policy_optimizers/sac/sac.py b/mbpo/optimizers/policy_optimizers/sac/sac.py
index bef1a7e..c49bed9 100644
--- a/mbpo/optimizers/policy_optimizers/sac/sac.py
+++ b/mbpo/optimizers/policy_optimizers/sac/sac.py
@@ -91,6 +91,11 @@ def __init__(self,
                  eval_environment: envs.Env | None = None,
                  episode_length_eval: int | None = None,
                  eval_key_fixed: bool = False,
+                 non_equidistant_time: bool = False,
+                 continuous_discounting: float = 0,
+                 min_time_between_switches: float = 0,
+                 max_time_between_switches: float = 0,
+                 env_dt: float = 0,
                  ):
         if min_replay_size >= num_timesteps:
             raise ValueError(
@@ -201,7 +206,13 @@ def normalize_fn(batch: PyTree, _: PyTree) -> PyTree:
 
         # Setup optimization
         self.losses = SACLosses(sac_network=self.sac_networks_model.get_sac_networks(), reward_scaling=reward_scaling,
-                                discounting=discounting, u_dim=self.u_dim, target_entropy=self.target_entropy)
+                                discounting=discounting, u_dim=self.u_dim, target_entropy=self.target_entropy,
+                                non_equidistant_time=non_equidistant_time,
+                                continuous_discounting=continuous_discounting,
+                                min_time_between_switches=min_time_between_switches,
+                                max_time_between_switches=max_time_between_switches,
+                                env_dt=env_dt,
+                                )
 
         self.alpha_update = gradient_update_fn(
             self.losses.alpha_loss, self.alpha_optimizer, pmap_axis_name=self._PMAP_AXIS_NAME)