fixed reward function -> less deflection is rewarding

reinforcement_learning/her_deep_Q_bridge.ipynb

@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 138,
    "metadata": {
     "scrolled": false
    },
@@ -172,7 +172,7 @@
     "\n",
     "        # Bridge is build\n",
     "        if np.all(self.location == self.goal) and self.state[np.split(self.location, 2)]:\n",
-    "            r = r + 1 / (self.structure() / (100 * max(x_range)**3 / (48 * self.ss.EI)))\n",
+    "            r = r + self.compute_reward()\n",
     "            done = True\n",
     "            return np.concatenate((self.state.ravel(), [self.deploy], scale_down(self.location, 0, max(self.height, self.length)), \n",
     "                                   scale_down(self.goal, 0, max(self.height - 1, self.length - 1)))), r, done\n",
@@ -180,7 +180,7 @@
     "        self.det_valid_actions()\n",
     "        if len(self.valid_actions) == 0:\n",
     "            done = True\n",
-    "            r -= 2\n",
+    "            r -= 0.5\n",
     "        \n",
     "        return np.concatenate((self.state.ravel(), [self.deploy], scale_down(self.location, 0, max(self.height, self.length)), \n",
     "                                   scale_down(self.goal, 0, max(self.height - 1, self.length - 1)))), r, done\n",
@@ -272,7 +272,6 @@
     "        return self.return_action(0)\n",
     "    \n",
     "    def structure(self):\n",
-    "        \n",
     "        # last node's x\n",
     "        x_range = self.ss.nodes_range('x')\n",
     "        length = max(x_range)\n",
@@ -288,6 +287,9 @@
     "        \n",
     "        return abs(self.ss.get_node_displacements(middle_node_id)[\"uy\"])\n",
     "    \n",
+    "    def compute_reward(self):\n",
+    "        return 1 / (self.structure() / (100 * max(self.ss.nodes_range('x'))**3 / (48 * self.ss.EI)))\n",
+    "    \n",
     "    def sample_action(self):\n",
     "        return np.random.randint(0, 9) \n",
     "    \n",
@@ -297,17 +299,17 @@
     "    \n",
     "\n",
     "        \n",
-    "# env = Environment(10, 8)\n",
+    "# env = Environment(7, 8)\n",
     "# env.reset()\n",
-    "# env.step(2)\n",
-    "# # env.step(8)\n",
-    "# # env.step(0)\n",
+    "# env.step(1)\n",
+    "# env.step(8)\n",
+    "# env.step(7)\n",
     "\n",
-    "# # env.step(8)\n",
-    "# print(env.view())\n",
-    "# print(env.location)\n",
+    "# # # env.step(8)\n",
+    "# # print(env.view())\n",
+    "# # print(env.location)\n",
     "\n",
-    "# int(env.state[np.split(env.location + np.array([ 1, 0]), 2)]) == 1\n"
+    "# # int(env.state[np.split(env.location + np.array([ 1, 0]), 2)]) == 1\n"
    ]
   },
   {
@@ -469,7 +471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
@@ -478,14 +480,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.106507352776\n"
+      "0.171960320003 max 6\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa6b53d3ac8>"
+       "<matplotlib.figure.Figure at 0x7fa70c571a90>"
       ]
      },
      "metadata": {},
@@ -495,14 +497,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0670201159182\n"
+      "1.45821822148 max 6\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa6bec2ef28>"
+       "<matplotlib.figure.Figure at 0x7fa6b5d0cc88>"
       ]
      },
      "metadata": {},
@@ -512,14 +514,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.052281676787\n"
+      "1.11455188288 max 6\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa6b4fa0320>"
+       "<matplotlib.figure.Figure at 0x7fa6bec63940>"
       ]
      },
      "metadata": {},
@@ -529,14 +531,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0171564638668\n"
+      "3.35875721064 max 6\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa6b4353e80>"
+       "<matplotlib.figure.Figure at 0x7fa6be128c88>"
       ]
      },
      "metadata": {},
@@ -546,14 +548,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0405713260877\n"
+      "1.25851633209 max 9\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa6b4d29f60>"
+       "<matplotlib.figure.Figure at 0x7fa6b5747e10>"
       ]
      },
      "metadata": {},
@@ -563,14 +565,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.052281676787\n"
+      "1.21851478584 max 9\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa6b4e59358>"
+       "<matplotlib.figure.Figure at 0x7fa6b4799dd8>"
       ]
      },
      "metadata": {},
@@ -580,14 +582,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0526057340714\n"
+      "4.34083728535 max 6\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa6b5861be0>"
+       "<matplotlib.figure.Figure at 0x7fa6be475ef0>"
       ]
      },
      "metadata": {},
@@ -597,14 +599,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.113213930011\n"
+      "1.34288039892 max 6\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa6b4fc7748>"
+       "<matplotlib.figure.Figure at 0x7fa6b5aeccf8>"
       ]
      },
      "metadata": {},
@@ -614,14 +616,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0224078583877\n"
+      "3.44586626525 max 9\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa6b4fe8b00>"
+       "<matplotlib.figure.Figure at 0x7fa6b5dd55c0>"
       ]
      },
      "metadata": {},
@@ -631,14 +633,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.09\n"
+      "6.84802915883 max 9\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa6b4f7c390>"
+       "<matplotlib.figure.Figure at 0x7fa6b56ce1d0>"
       ]
      },
      "metadata": {},
@@ -648,14 +650,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0220719354642\n"
+      "5.0362888059 max 9\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa71626fdd8>"
+       "<matplotlib.figure.Figure at 0x7fa6be4cd3c8>"
       ]
      },
      "metadata": {},
@@ -665,14 +667,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.052281676787\n"
+      "1.3356077076 max 6\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa7140960b8>"
+       "<matplotlib.figure.Figure at 0x7fa6b4767860>"
       ]
      },
      "metadata": {},
@@ -682,14 +684,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.052281676787\n"
+      "3.51262552585 max 6\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa6b4b52828>"
+       "<matplotlib.figure.Figure at 0x7fa70c571d68>"
       ]
      },
      "metadata": {},
@@ -699,14 +701,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.20106461425\n"
+      "1.0 max 6\n"
      ]
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa6b50b4cc0>"
+       "<matplotlib.figure.Figure at 0x7fa6b50342b0>"
       ]
      },
      "metadata": {},
@@ -716,14 +718,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0343049339466\n"
+      "1.72144440521 max 6\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa6b4d14518>"
+       "<matplotlib.figure.Figure at 0x7fa6b57bae10>"
       ]
      },
      "metadata": {},
@@ -733,14 +735,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.256425962666\n"
+      "1.3356077076 max 6\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa6b4e58e80>"
+       "<matplotlib.figure.Figure at 0x7fa6b4c97e48>"
       ]
      },
      "metadata": {},
@@ -750,14 +752,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.052281676787\n"
+      "1.37498296051 max 6\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa6b4f8bb70>"
+       "<matplotlib.figure.Figure at 0x7fa6b47d54a8>"
       ]
      },
      "metadata": {},
@@ -767,14 +769,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.052281676787\n"
+      "6.94500211953 max 9\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa6bebdbe80>"
+       "<matplotlib.figure.Figure at 0x7fa6b509a4e0>"
       ]
      },
      "metadata": {},
@@ -784,14 +786,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0661111111111\n"
+      "1.72144440521 max 6\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa6b5451cf8>"
+       "<matplotlib.figure.Figure at 0x7fa6b45d8160>"
       ]
      },
      "metadata": {},
@@ -801,14 +803,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.402155400291\n"
+      "0.171960320003 max 6\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa6b4b091d0>"
+       "<matplotlib.figure.Figure at 0x7fa6b414f908>"
       ]
      },
      "metadata": {},
@@ -818,51 +820,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0502647454218\n"
+      "0.652469078744 max 9\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fa6b51e1198>"
+       "<matplotlib.figure.Figure at 0x7fa6b4640ba8>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-0.0966130907655 train_count 4 loss 1.39015 temp 5 n_success 5\n",
-      "0.036334618792\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-130-0438b881b444>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    100\u001b[0m                         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstructure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m                         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m                         \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m                     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/code/python/anaStruct/anastruct/fem/system.py\u001b[0m in \u001b[0;36mshow_structure\u001b[0;34m(self, verbosity, scale, offset, figsize, show, supports)\u001b[0m\n\u001b[1;32m    871\u001b[0m         \"\"\"\n\u001b[1;32m    872\u001b[0m         \u001b[0mfigsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigsize\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfigsize\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 873\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbosity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msupports\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    874\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    875\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mshow_bending_moment\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfactor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbosity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/code/python/anaStruct/anastruct/fem/plotter.py\u001b[0m in \u001b[0;36mplot_structure\u001b[0;34m(self, figsize, verbosity, show, supports, scale, offset, gridplot)\u001b[0m\n\u001b[1;32m    376\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__point_load_patch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_plot_range\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbosity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__moment_load_patch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_plot_range\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 378\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    379\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/code/python/anaStruct/anastruct/fem/plotter.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    404\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackend\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"mpl\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 406\u001b[0;31m             \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    407\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackend\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"ipt\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m             \u001b[0miplot_mpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    249\u001b[0m     \"\"\"\n\u001b[1;32m    250\u001b[0m     \u001b[0;32mglobal\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/lib/python3.6/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfigure_manager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m             \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0mshow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/lib/python3.6/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, *objs, **kwargs)\u001b[0m\n\u001b[1;32m    295\u001b[0m             \u001b[0mpublish_display_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 297\u001b[0;31m             \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    298\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m                 \u001b[0;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/lib/python3.6/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mformat\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0;31m# FIXME: log the exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<decorator-gen-9>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/lib/python3.6/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    222\u001b[0m     \u001b[0;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/lib/python3.6/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/lib/python3.6/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/lib/python3.6/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/lib/python3.6/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2257\u001b[0m                 \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2258\u001b[0m                 \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2259\u001b[0;31m                 **kwargs)\n\u001b[0m\u001b[1;32m   2260\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2261\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    524\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    525\u001b[0m             _png.write_png(renderer._renderer, filename_or_obj,\n\u001b[0;32m--> 526\u001b[0;31m                            self.figure.dpi, metadata=metadata)\n\u001b[0m\u001b[1;32m    527\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    528\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
     }
    ],
    "source": [
@@ -965,8 +934,8 @@
     "                if i == index:\n",
     "                    done = True\n",
     "                    if max(env.ss.nodes_range('x')) > 3:\n",
-    "                        r = env.structure()\n",
-    "                        print(r)\n",
+    "                        r = env.compute_reward()\n",
+    "                        print(r, \"max\", max(env.ss.nodes_range('x')))\n",
     "                        env.ss.show_structure()\n",
     "                        \n",
     "                    else:\n",