diff --git a/env-data/railway/example_network_000.pkl b/env-data/railway/example_network_000.pkl
index ab7764b1ea250633e5a0cd2ec9b93a4e07479c9f..e102e21735416747cb8bd9f231ce6e20fdf514c0 100644
Binary files a/env-data/railway/example_network_000.pkl and b/env-data/railway/example_network_000.pkl differ
diff --git a/env-data/railway/example_network_001.pkl b/env-data/railway/example_network_001.pkl
index af9dc43246449562065be64c750a06c75c3e7571..a9c5cc97c9c4bf4159db2134756f17fa0c4fce87 100644
Binary files a/env-data/railway/example_network_001.pkl and b/env-data/railway/example_network_001.pkl differ
diff --git a/env-data/railway/example_network_002.pkl b/env-data/railway/example_network_002.pkl
index d39e44798066e0a0b753006775c5727326fc58da..37647ac2871801d2d08fd65276889e2b232c1170 100644
Binary files a/env-data/railway/example_network_002.pkl and b/env-data/railway/example_network_002.pkl differ
diff --git a/examples/demo.py b/examples/demo.py
index a53370aac5b3668cb12f7f794b1d91d563ef96e4..c2485de46902304d375f80b0a3d689b8a18b6d0f 100644
--- a/examples/demo.py
+++ b/examples/demo.py
@@ -132,39 +132,16 @@ class Demo:
         handle = self.env.get_agent_handles()
         return handle
 
-    def run_demo(self, max_nbr_of_steps=100):
+    def run_demo(self, max_nbr_of_steps=30):
         action_dict = dict()
-        time_obs = deque(maxlen=2)
-        action_prob = [0] * 4
-        agent_obs = [None] * self.env.get_num_agents()
-        agent_next_obs = [None] * self.env.get_num_agents()
 
         # Reset environment
         obs = self.env.reset(False, False)
 
-        for a in range(self.env.get_num_agents()):
-            data, distance = self.env.obs_builder.split_tree(tree=np.array(obs[a]), num_features_per_node=5, current_depth=0)
-
-            data = norm_obs_clip(data)
-            distance = norm_obs_clip(distance)
-            obs[a] = np.concatenate((data, distance))
-
-        for i in range(2):
-            time_obs.append(obs)
-
-        # env.obs_builder.util_print_obs_subtree(tree=obs[0], num_elements_per_node=5)
-        for a in range(self.env.get_num_agents()):
-            agent_obs[a] = np.concatenate((time_obs[0][a], time_obs[1][a]))
-
         for step in range(max_nbr_of_steps):
-
-            time.sleep(.2)
-
-            # print(step)
             # Action
             for a in range(self.env.get_num_agents()):
-                action = np.random.choice(self.action_size) #self.agent.act(agent_obs[a])
-                action_prob[action] += 1
+                action = 2 #np.random.choice(self.action_size) #self.agent.act(agent_obs[a])
                 action_dict.update({a: action})
 
             print(action_dict)
@@ -173,20 +150,7 @@ class Demo:
 
             # Environment step
             next_obs, all_rewards, done, _ = self.env.step(action_dict)
-            for a in range(self.env.get_num_agents()):
-                data, distance = self.env.obs_builder.split_tree(tree=np.array(next_obs[a]), num_features_per_node=5,
-                                                                 current_depth=0)
-                data = norm_obs_clip(data)
-                distance = norm_obs_clip(distance)
-                next_obs[a] = np.concatenate((data, distance))
-
-            # Update replay buffer and train agent
-            for a in range(self.env.get_num_agents()):
-                agent_next_obs[a] = np.concatenate((time_obs[0][a], time_obs[1][a]))
-
-            time_obs.append(next_obs)
 
-            agent_obs = agent_next_obs.copy()
             if done['__all__']:
                 break
 
diff --git a/notebooks/Editor2.ipynb b/notebooks/Editor2.ipynb
index 20286e886c5d73f0ce427e8feeb209a8ad99c000..71b74b793e0ae3ba41dcb1f18ff501eb10595667 100644
--- a/notebooks/Editor2.ipynb
+++ b/notebooks/Editor2.ipynb
@@ -9,9 +9,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2"
@@ -19,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -54,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -97,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "metadata": {
     "scrolled": false
    },
@@ -105,7 +114,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7b66ea9348c9477f881ff27456987363",
+       "model_id": "31e3248d9a0e4b5da8f2439abd13558d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -123,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 15,
    "metadata": {
     "scrolled": false
    },
@@ -131,7 +140,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ffa0f869fe8a4921a7415384b75c1ded",
+       "model_id": "c22754b330ce490383eb05972bc96afe",
        "version_major": 2,
        "version_minor": 0
       },
@@ -150,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -159,7 +168,7 @@
        "(0, 0)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }