diff --git a/test.py b/test.py
index 17accf5..1a3ee7d 100644
--- a/test.py
+++ b/test.py
@@ -12,19 +12,30 @@ from sb3_trl.trl_pg import TRL_PG
 import columbus
 
 
-def main(env_name='ColumbusEasyObstacles-v0'):
+def main(env_name='ColumbusEasierObstacles-v0'):
     env = gym.make(env_name)
     ppo_latent_sde = PPO(
         "MlpPolicy",
         env,
-        verbose=1,
+        verbose=0,
         tensorboard_log="./logs_tb/"+env_name+"/ppo_latent_sde/",
         use_sde=True,
         sde_sample_freq=30*15,
-        ent_coef=0.0032,
-        vf_coef=0.0005,
-        gamma=0.95,
-        learning_rate=0.02
+        ent_coef=0.0016/1.25, #0.0032
+        vf_coef=0.00025/2, #0.0005
+        gamma=0.99, # 0.95
+        learning_rate=0.005/5 # 0.015
+    )
+    sac_latent_sde = SAC(
+        "MlpPolicy",
+        env,
+        verbose=0,
+        tensorboard_log="./logs_tb/"+env_name+"/sac_latent_sde/",
+        use_sde=True,
+        sde_sample_freq=30*15,
+        ent_coef=0.0016, #0.0032
+        gamma=0.99, # 0.95
+        learning_rate=0.001 # 0.015
     )
     #trl = TRL_PG(
     #    "MlpPolicy",
@@ -33,8 +44,10 @@ def main(env_name='ColumbusEasyObstacles-v0'):
     #    tensorboard_log="./logs_tb/"+env_name+"/trl_pg/",
     #)
 
-    print('PPO_LATENT_SDE:')
-    testModel(ppo_latent_sde, 100000, showRes = True, saveModel=True, n_eval_episodes=0)
+    #print('PPO_LATENT_SDE:')
+    #testModel(ppo_latent_sde, 1000000, showRes = True, saveModel=True, n_eval_episodes=3)
+    print('SAC_LATENT_SDE:')
+    testModel(ppo_latent_sde, 250000, showRes = True, saveModel=True, n_eval_episodes=0)
     #print('TRL_PG:')
     #testModel(trl)
 
@@ -45,7 +58,7 @@ def testModel(model, timesteps=150000, showRes=False, saveModel=False, n_eval_ep
 
     if saveModel:
         now = datetime.datetime.now().strftime('%d.%m.%Y-%H:%M')
-        model.save(model.tensorboard_log.replace('./logs_tb/','').replace('/','_')+now+'.zip')
+        model.save('models/'+model.tensorboard_log.replace('./logs_tb/','').replace('/','_')+now+'.zip')
 
     if n_eval_episodes:
         mean_reward, std_reward = evaluate_policy(model, env, n_eval_episodes=n_eval_episodes, deterministic=False)