diff --git a/test.py b/test.py
index 5042e33..8f0e7fc 100755
--- a/test.py
+++ b/test.py
@@ -1,8 +1,5 @@
 #!/usr/bin/python3
 import gym
-from gym.envs.registration import register
-import numpy as np
-import os
 import time
 import datetime
 
@@ -67,15 +64,15 @@ def main(env_name='ColumbusCandyland_Aux10-v0', timesteps=1_000_000, showRes=Tru
     #          saveModel, n_eval_episodes)
 
 
-def full(env_name='ColumbusCandyland_Aux10-v0', timesteps=35_000, saveModel=True, n_eval_episodes=4):
+def full(env_name='ColumbusCandyland_Aux10-v0', timesteps=200_000, saveModel=True, n_eval_episodes=4):
     env = gym.make(env_name)
     use_sde = False
-    skip_num = 8  # 10 (/ start at index)
-    sac = True
+    skip_num = 4  # 10 (/ start at index)
+    sac = False
     Model = [PPO, SAC][sac]
     Policy = [MlpPolicyPPO, MlpPolicySAC][sac]
-    #projection = FrobeniusProjectionLayer()
-    projection = BaseProjectionLayer()
+    projection = FrobeniusProjectionLayer()
+    #projection = BaseProjectionLayer()
 
     gen = enumerate(get_legal_setups(
         allowedEPTs=[EnforcePositiveType.SOFTPLUS, EnforcePositiveType.ABS]))
@@ -87,21 +84,21 @@ def full(env_name='ColumbusCandyland_Aux10-v0', timesteps=35_000, saveModel=True
         model = Model(
             Policy,
             env,
-            # projection=projection,
+            projection=projection,
             policy_kwargs={'dist_kwargs': {'neural_strength': ps, 'cov_strength': cs, 'parameterization_type':
-                                           pt, 'enforce_positive_type': ept, 'prob_squashing_type': ProbSquashingType.NONE}},
+                                           pt, 'enforce_positive_type': ept, 'prob_squashing_type': ProbSquashingType.TANH}},
             verbose=0,
             tensorboard_log=root_path+"/logs_tb/" +
-            env_name+"/"+['ppo', 'sac'][sac]+"_" +
+            env_name+"/"+['ppo', 'sac'][sac]+"_" + 'TANH_' +
             ("_".join([str(s) for s in setup])+['', '_sde'][use_sde])+"/",
-            # learning_rate=3e-4,
-            # gamma=0.99,
-            # gae_lambda=0.95,
-            # normalize_advantage=True,
-            # ent_coef=0.02,  # 0.1
-            # vf_coef=0.5,
+            learning_rate=3e-4,
+            gamma=0.99,
+            gae_lambda=0.95,
+            normalize_advantage=True,
+            ent_coef=0.02,  # 0.1
+            vf_coef=0.5,
             use_sde=use_sde,  # False
-            # clip_range=1  # 0.2,
+            clip_range=1  # 0.2,
         )
 
         testModel(model, timesteps, False,
@@ -152,5 +149,5 @@ if __name__ == '__main__':
     # main('ColumbusJustState-v0')
     # main('ColumbusStateWithBarriers-v0')
     # full('ColumbusEasierObstacles-v0')
-    # full('ColumbusStateWithBarriers-v0')
-    full('LunarLanderContinuous-v2')
+    full('ColumbusStateWithBarriers-v0')
+    # full('LunarLanderContinuous-v2')