Feature: adding new_reward utilisation option to training analysis notebook

ActionSpace_analysis.ipynb

@@ -347,7 +347,7 @@
    "outputs": [],
    "source": [
     "# Uncomment the line of code below to evaluate a different reward function\n",
-    "# nr.new_reward(df, l_center_line, 'reward.reward_sample') #, verbose=True)"
+    "# nr.new_reward(df, l_track, 'reward.reward_sample') #, verbose=True)"
    ]
   },
   {

ActionSpace_analysis.py

@@ -173,7 +173,7 @@
 
 # +
 # Uncomment the line of code below to evaluate a different reward function
-# nr.new_reward(df, l_center_line, 'reward.reward_sample') #, verbose=True)
+# nr.new_reward(df, l_track, 'reward.reward_sample') #, verbose=True)
 # -
 
 simulation_agg = au.simulation_agg(df)

Training_analysis.ipynb

@@ -684,6 +684,17 @@
     "The higher the value, the more stable the model is on a given track."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Uncomment the line of code below to evaluate a different reward function\n",
+    "#l_track = track.center_line\n",
+    "#nr.new_reward(df, l_track, 'reward.reward_sample') #, verbose=True)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 12,

Training_analysis.py

@@ -208,6 +208,11 @@
 #
 # The higher the value, the more stable the model is on a given track.
 
+# +
+# Uncomment the line of code below to evaluate a different reward function
+#l_track = track.center_line
+#nr.new_reward(df, l_track, 'reward.reward_sample') #, verbose=True)
+
 # +
 simulation_agg = au.simulation_agg(df)
 try: