diff --git a/spinup/algos/pytorch/sac/sac.py b/spinup/algos/pytorch/sac/sac.py
index 8cd0f5ae3..354aad9c6 100644
--- a/spinup/algos/pytorch/sac/sac.py
+++ b/spinup/algos/pytorch/sac/sac.py
@@ -53,7 +53,7 @@ def sample_batch(self, batch_size=32, sample_mode=1, sequence_length=1):
 
 def sac(env_fn, actor_critic=core.MLPActorCritic, ac_kwargs=dict(), seed=0,
         steps_per_epoch=4000, epochs=100, replay_size=int(1e6), gamma=0.99,
-        polyak=0.995, lr=1e-3, alpha=0.2, batch_size=100, start_steps=10000,
+        polyak=0.995, lr=1e-3, alpha_init=0.2, batch_size=100, start_steps=10000,
         update_after=1000, update_every=50, num_test_episodes=10, max_ep_len=1000,
         logger_kwargs=dict(), save_freq=1, initial_actions="random", save_buffer=False, sample_mode=1, automatic_entropy_tuning=False):
     """
@@ -194,6 +194,8 @@ def compute_loss_q(data):
         if automatic_entropy_tuning==True:
             # get updated alpha
             alpha = log_alpha.exp()
+        else:
+            alpha = alpha_init
 
         # Bellman backup for Q functions
         with torch.no_grad():
@@ -227,6 +229,9 @@ def compute_loss_pi(data):
         if automatic_entropy_tuning==True:
             # get updated alpha
             alpha = log_alpha.exp()
+        else:
+            alpha = alpha_init
+
         # Entropy-regularized policy loss
         loss_pi = (alpha * logp_pi - q_pi).mean()
         # Useful info for logging
@@ -282,7 +287,7 @@ def update(data):
         else:
             # alpha_info = dict(LogAlpha=alpha.detach().numpy())
             # logger.store(LossAlpha=alpha_loss.item(), **alpha_info)
-            logger.store(LossAlpha=0, LogAlpha=alpha)
+            logger.store(LossAlpha=0, LogAlpha=alpha_init)
         # Unfreeze Q-networks so you can optimize it at next (DDPG, SAC, ...) step.
         for p in q_params:
             p.requires_grad = True
diff --git a/spinup/examples/pytorch/Hybrid_SAC.py b/spinup/examples/pytorch/Hybrid_SAC.py
index 938ca41c6..b3434ddc3 100644
--- a/spinup/examples/pytorch/Hybrid_SAC.py
+++ b/spinup/examples/pytorch/Hybrid_SAC.py
@@ -18,8 +18,8 @@
             os.makedirs(output_dir)
         logger_kwargs = dict(output_dir=output_dir, exp_name=exp_name)
         sac(env_fn, ac_kwargs={}, seed=0, steps_per_epoch=100, epochs=200, replay_size=1000000, gamma=0.99, polyak=0.995,
-            lr=0.01, alpha=0.001, batch_size=100, start_steps=1000, update_after=1000, update_every=100, num_test_episodes=2,
-            max_ep_len=np.inf, logger_kwargs=logger_kwargs, save_freq=1, initial_actions="zero", save_buffer=True, sample_mode = 1, automatic_entropy_tuning=True)
+            lr=0.01, alpha_init=0.001, batch_size=100, start_steps=1000, update_after=1000, update_every=100, num_test_episodes=2,
+            max_ep_len=np.inf, logger_kwargs=logger_kwargs, save_freq=1, initial_actions="zero", save_buffer=True, sample_mode = 1, automatic_entropy_tuning=False)
     else:
         output_dir='/home/mahdi/ETHZ/codes/spinningup/spinup/examples/pytorch/logs/'+exp_name
         env_loaded, get_action = load_policy_and_env(output_dir)