diff --git a/test.py b/test.py
old mode 100644
new mode 100755
index 1a3ee7d..e5cdaab
--- a/test.py
+++ b/test.py
@@ -1,6 +1,8 @@
+#!/bin/python3
 import gym
 from gym.envs.registration import register
 import numpy as np
+import os
 import time
 import datetime
 
@@ -11,32 +13,34 @@ from stable_baselines3.common.policies import ActorCriticCnnPolicy, ActorCriticP
 from sb3_trl.trl_pg import TRL_PG
 import columbus
 
+#root_path = os.getcwd()
+root_path = '.'
 
-def main(env_name='ColumbusEasierObstacles-v0'):
+def main(env_name='ColumbusStateWithBarriers-v0'):
     env = gym.make(env_name)
     ppo_latent_sde = PPO(
         "MlpPolicy",
         env,
         verbose=0,
-        tensorboard_log="./logs_tb/"+env_name+"/ppo_latent_sde/",
-        use_sde=True,
+        tensorboard_log=root_path+"/logs_tb/"+env_name+"/ppo_latent_sde/",
+        #use_sde=True,
         sde_sample_freq=30*15,
-        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
+        #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=root_path+"/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",
     #    env,
@@ -44,10 +48,10 @@ def main(env_name='ColumbusEasierObstacles-v0'):
     #    tensorboard_log="./logs_tb/"+env_name+"/trl_pg/",
     #)
 
-    #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('PPO_LATENT_SDE:')
+    testModel(ppo_latent_sde, 25000, showRes = True, saveModel=True, n_eval_episodes=3)
+    #print('SAC_LATENT_SDE:')
+    #testModel(sac_latent_sde, 250000, showRes = True, saveModel=True, n_eval_episodes=0)
     #print('TRL_PG:')
     #testModel(trl)
 
@@ -58,7 +62,8 @@ 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('models/'+model.tensorboard_log.replace('./logs_tb/','').replace('/','_')+now+'.zip')
+        loc = root_path+'/models/'+model.tensorboard_log.replace(root_path+'/logs_tb/','').replace('/','_')+now+'.zip'
+        model.save(loc)
 
     if n_eval_episodes:
         mean_reward, std_reward = evaluate_policy(model, env, n_eval_episodes=n_eval_episodes, deterministic=False)