diff --git a/main.ipynb b/main.ipynb
index d272c83..ed19025 100644
--- a/main.ipynb
+++ b/main.ipynb
@@ -2545,13 +2545,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 115,
    "metadata": {
     "pycharm": {
      "is_executing": true
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-1.53249554e-06, -1.91561943e-06, -2.39452428e-06, -2.99315535e-06,\n",
+       "       -3.74144419e-06, -4.67680524e-06, -5.84600655e-06, -7.30750819e-06,\n",
+       "       -9.13438523e-06, -1.14179815e-05, -1.42724769e-05, -1.78405962e-05,\n",
+       "       -2.23007452e-05, -2.78759315e-05, -3.48449144e-05, -4.35561430e-05,\n",
+       "       -5.44451787e-05, -6.80564734e-05, -8.50705917e-05, -1.06338240e-04,\n",
+       "       -1.32922800e-04, -1.66153499e-04, -2.07691874e-04, -2.59614843e-04,\n",
+       "       -3.24518554e-04, -4.05648192e-04, -5.07060240e-04, -6.33825300e-04,\n",
+       "       -7.92281625e-04, -9.90352031e-04, -1.23794004e-03, -1.54742505e-03,\n",
+       "       -1.93428131e-03, -2.41785164e-03, -3.02231455e-03, -3.77789319e-03,\n",
+       "       -4.72236648e-03, -5.90295810e-03, -7.37869763e-03, -9.22337204e-03,\n",
+       "       -1.15292150e-02, -1.44115188e-02, -1.80143985e-02, -2.25179981e-02,\n",
+       "       -2.81474977e-02, -3.51843721e-02, -4.39804651e-02, -5.49755814e-02,\n",
+       "       -6.87194767e-02, -8.58993459e-02, -1.07374182e-01, -1.34217728e-01,\n",
+       "       -1.67772160e-01, -2.09715200e-01, -2.62144000e-01, -3.27680000e-01,\n",
+       "       -4.09600000e-01, -5.12000000e-01, -6.40000000e-01, -6.40000000e-01,\n",
+       "       -8.00000000e-01, -8.00000000e-01, -1.00000000e+00, -1.00000000e+00,\n",
+       "       -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00,\n",
+       "       -1.00000000e+00, -1.00000000e+00])"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "calculate_q_reword(\n",
     "    _board_history, gamma=0.8, who_won_fraction=1, final_score_fraction=0\n",
@@ -2560,13 +2588,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 116,
    "metadata": {
     "pycharm": {
      "is_executing": true
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  1.53941436,  -1.82573204,   1.46783494,  -1.91520632,\n",
+       "         1.3559921 ,  -4.55500988,   0.55623766,  -3.05470293,\n",
+       "        -0.06837866,  -6.33547333,   0.83065834,  -2.71167707,\n",
+       "         0.36040366,  -3.29949543,   2.12563071,  -3.59296161,\n",
+       "         6.75879799,   4.69849749,   9.62312186,   5.77890232,\n",
+       "        13.47362791,   8.09203488,  13.8650436 ,   1.0813045 ,\n",
+       "         5.10163063,   0.12703829,   3.90879786,   1.13599733,\n",
+       "         5.16999666,  -4.78750418,   0.26561978,  -3.41797528,\n",
+       "         1.9775309 ,  -3.77808638,  -0.97260797,  -7.46575996,\n",
+       "         1.91780005,  -1.35274994,   4.55906257,  -8.05117178,\n",
+       "         1.18603527,  -4.76745591,   0.29068011,  -3.38664986,\n",
+       "        -0.48331232,  -4.3541404 ,   3.3073245 ,   0.38415562,\n",
+       "         4.23019452,   1.53774316,   8.17217894,   1.46522368,\n",
+       "         5.5815296 ,  -4.273088  ,  -1.59136   , -15.7392    ,\n",
+       "        -8.424     , -24.28      , -26.6       , -26.6       ,\n",
+       "       -17.        , -17.        ,   0.        ,   0.        ,\n",
+       "         0.        ,   0.        ,   0.        ,   0.        ,\n",
+       "         0.        ,   0.        ])"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "calculate_q_reword(\n",
     "    _board_history, gamma=0.8, who_won_fraction=0, final_score_fraction=0\n",
@@ -2575,7 +2631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 117,
    "metadata": {
     "pycharm": {
      "is_executing": true
@@ -2602,7 +2658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 118,
    "metadata": {
     "pycharm": {
      "is_executing": true
@@ -2659,7 +2715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 119,
    "metadata": {
     "pycharm": {
      "is_executing": true
@@ -2675,14 +2731,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 120,
    "metadata": {
     "pycharm": {
      "is_executing": true
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((70, 100, 8, 8), (70, 100, 2))"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "_board_history, _action_history = simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))\n",
     "_board_history.shape, _action_history.shape"
@@ -2690,14 +2757,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 121,
    "metadata": {
     "pycharm": {
      "is_executing": true
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6.31 ms ± 334 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
+      "peak memory: 580.32 MiB, increment: 0.04 MiB\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "def action_to_q_learning_format(\n",
     "    board_history: np.ndarray, action_history: np.ndarray\n",
@@ -2734,14 +2830,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 122,
    "metadata": {
     "pycharm": {
      "is_executing": true
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "46.4 ms ± 1.36 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "peak memory: 576.92 MiB, increment: 0.01 MiB\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(2, 2, 2, 70, 100, 2, 8, 8)"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "def build_symetry_action(\n",
     "    board_history: np.ndarray, action_history: np.ndarray\n",
@@ -2773,7 +2888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 123,
    "metadata": {
     "pycharm": {
      "is_executing": true
@@ -2810,7 +2925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 124,
    "metadata": {
     "pycharm": {
      "is_executing": true
@@ -3074,7 +3189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 125,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -3084,7 +3199,18 @@
      "is_executing": true
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(70, 10, 8, 8)"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "_train_boards, _train_actions = simulate_game(10, (RandomPolicy(0), RandomPolicy(0)))\n",
     "_action_possible = ~np.all(_train_actions[:, :] == -1, axis=2)\n",
@@ -3138,14 +3264,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 126,
    "metadata": {
     "pycharm": {
      "is_executing": true
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'QL-M-G09-WW00-FSF10-DQLSimple-MSELoss'"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ql_policy1 = QLPolicy(\n",
     "    0.92,\n",
@@ -3160,14 +3297,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 141,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ql_policys = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
    "metadata": {
     "pycharm": {
      "is_executing": true
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'QL-M-G09-WW00-FSF10-DQLSimple-MSELoss'"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ql_policy1 = QLPolicy(\n",
     "    0.92,\n",
@@ -3177,19 +3336,31 @@
     "    who_won_fraction=0,\n",
     "    final_score_fraction=1,\n",
     ")\n",
+    "ql_policys.append(ql_policy1)\n",
     "ql_policy1.policy_name"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 143,
    "metadata": {
     "pycharm": {
      "is_executing": true
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'QL-M-G08-WW00-FSF10-DQLSimple-MSELoss'"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ql_policy2 = QLPolicy(\n",
     "    0.92,\n",
@@ -3199,19 +3370,31 @@
     "    who_won_fraction=0,\n",
     "    final_score_fraction=1,\n",
     ")\n",
+    "ql_policys.append(ql_policy2)\n",
     "ql_policy2.policy_name"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 144,
    "metadata": {
     "pycharm": {
      "is_executing": true
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'QL-M-G10-WW00-FSF10-DQLSimple-MSELoss'"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ql_policy3 = QLPolicy(\n",
     "    0.92,\n",
@@ -3221,19 +3404,31 @@
     "    who_won_fraction=0,\n",
     "    final_score_fraction=1,\n",
     ")\n",
+    "ql_policys.append(ql_policy3)\n",
     "ql_policy3.policy_name"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 145,
    "metadata": {
     "pycharm": {
      "is_executing": true
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'QL-M-G09-WW10-FSF00-DQLSimple-MSELoss'"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ql_policy4 = QLPolicy(\n",
     "    0.92,\n",
@@ -3243,12 +3438,13 @@
     "    who_won_fraction=1,\n",
     "    final_score_fraction=0,\n",
     ")\n",
+    "ql_policys.append(ql_policy4)\n",
     "ql_policy4.policy_name"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 146,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -3258,7 +3454,18 @@
      "is_executing": true
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'QL-M-G09-WW03-FSF03-DQLSimple-MSELoss'"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ql_policy5 = QLPolicy(\n",
     "    0.95,\n",
@@ -3268,12 +3475,13 @@
     "    who_won_fraction=0.3,\n",
     "    final_score_fraction=0.3,\n",
     ")\n",
+    "ql_policys.append(ql_policy5)\n",
     "ql_policy5.policy_name"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 147,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -3283,7 +3491,18 @@
      "is_executing": true
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'QL-M-G09-WW03-FSF07-DQLSimple-MSELoss'"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ql_policy6 = QLPolicy(\n",
     "    0.95,\n",
@@ -3293,19 +3512,31 @@
     "    who_won_fraction=0.3,\n",
     "    final_score_fraction=0.65,\n",
     ")\n",
+    "ql_policys.append(ql_policy6)\n",
     "ql_policy6.policy_name"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 148,
    "metadata": {
     "pycharm": {
      "is_executing": true
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'QL-M-G09-WW02-FSF07-DQLSimple-MSELoss'"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ql_policy7 = QLPolicy(\n",
     "    0.95,\n",
@@ -3315,19 +3546,31 @@
     "    who_won_fraction=0.2,\n",
     "    final_score_fraction=0.65,\n",
     ")\n",
-    "ql_policy6.policy_name"
+    "ql_policys.append(ql_policy7)\n",
+    "ql_policy7.policy_name"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 149,
    "metadata": {
     "pycharm": {
      "is_executing": true
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'QL-M-G08-WW00-FSF10-DQLNet-MSELoss'"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ql_policy8 = QLPolicy(\n",
     "    0.92,\n",
@@ -3337,19 +3580,31 @@
     "    who_won_fraction=0,\n",
     "    final_score_fraction=1,\n",
     ")\n",
-    "ql_policy2.policy_name"
+    "ql_policys.append(ql_policy8)\n",
+    "ql_policy8.policy_name"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 150,
    "metadata": {
     "pycharm": {
      "is_executing": true
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'QL-M-G10-WW00-FSF10-DQLNet-MSELoss'"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ql_policy9 = QLPolicy(\n",
     "    0.92,\n",
@@ -3359,19 +3614,31 @@
     "    who_won_fraction=0,\n",
     "    final_score_fraction=1,\n",
     ")\n",
-    "ql_policy3.policy_name"
+    "ql_policys.append(ql_policy9)\n",
+    "ql_policy9.policy_name"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 151,
    "metadata": {
     "pycharm": {
      "is_executing": true
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'QL-M-G09-WW10-FSF00-DQLNet-MSELoss'"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ql_policy10 = QLPolicy(\n",
     "    0.92,\n",
@@ -3381,12 +3648,13 @@
     "    who_won_fraction=1,\n",
     "    final_score_fraction=0,\n",
     ")\n",
-    "ql_policy4.policy_name"
+    "ql_policys.append(ql_policy10)\n",
+    "ql_policy10.policy_name"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 152,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -3396,7 +3664,18 @@
      "is_executing": true
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'QL-M-G09-WW03-FSF03-DQLNet-MSELoss'"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ql_policy11 = QLPolicy(\n",
     "    0.95,\n",
@@ -3406,12 +3685,13 @@
     "    who_won_fraction=0.3,\n",
     "    final_score_fraction=0.3,\n",
     ")\n",
-    "ql_policy5.policy_name"
+    "ql_policys.append(ql_policy11)\n",
+    "ql_policy11.policy_name"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 153,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -3421,7 +3701,18 @@
      "is_executing": true
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'QL-M-G09-WW03-FSF07-DQLNet-MSELoss'"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ql_policy12 = QLPolicy(\n",
     "    0.95,\n",
@@ -3431,19 +3722,31 @@
     "    who_won_fraction=0.3,\n",
     "    final_score_fraction=0.65,\n",
     ")\n",
-    "ql_policy6.policy_name"
+    "ql_policys.append(ql_policy12)\n",
+    "ql_policy12.policy_name"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 154,
    "metadata": {
     "pycharm": {
      "is_executing": true
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'QL-M-G09-WW02-FSF07-DQLNet-MSELoss'"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ql_policy13 = QLPolicy(\n",
     "    0.95,\n",
@@ -3453,7 +3756,8 @@
     "    who_won_fraction=0.2,\n",
     "    final_score_fraction=0.65,\n",
     ")\n",
-    "ql_policy6.policy_name"
+    "ql_policys.append(ql_policy13)\n",
+    "ql_policy13.policy_name"
    ]
   },
   {
@@ -3480,26 +3784,39 @@
     },
     "tags": []
    },
-   "outputs": [],
-   "source": [
-    "ql_policy = ql_policy1\n",
-    "ql_policy.load()\n",
-    "ql_policy.train(15, 10, 1000, 250, [RandomPolicy(0), GreedyPolicy(0)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "is_executing": true
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d5b65d806968471e925990e0a73b862c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1 [00:00<?, ?epoch/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
-    "tags": []
-   },
-   "outputs": [],
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec4fbeb7458d4ed98cd863dc0eda19ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?batch/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "for i in range(100):\n",
-    "    for ql_policy in [ql_policy8, ql_policy9, ql_policy10, ql_policy11, ql_policy12, ql_policy13]:\n",
+    "    for ql_policy in ql_policys:\n",
     "        ql_policy.load()\n",
     "        ql_policy.train(1, 10, 1000, 250, [RandomPolicy(0), GreedyPolicy(0)])"
    ]
diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLNet-MSELoss-1.pickle b/training_data/QL-M-G08-WW00-FSF10-DQLNet-MSELoss-1.pickle
deleted file mode 100644
index a9c71a4..0000000
--- a/training_data/QL-M-G08-WW00-FSF10-DQLNet-MSELoss-1.pickle
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9ce3f0082289c865f812f8a340c95ac3965bebc4f4865c5e97ddabceeb34d841
-size 505
diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLNet-MSELoss-1.torch b/training_data/QL-M-G08-WW00-FSF10-DQLNet-MSELoss-1.torch
deleted file mode 100644
index 778adf6..0000000
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diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLNet-MSELoss-2.pickle b/training_data/QL-M-G08-WW00-FSF10-DQLNet-MSELoss-2.pickle
deleted file mode 100644
index cc7231d..0000000
--- a/training_data/QL-M-G08-WW00-FSF10-DQLNet-MSELoss-2.pickle
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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-size 911
diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLNet-MSELoss-2.torch b/training_data/QL-M-G08-WW00-FSF10-DQLNet-MSELoss-2.torch
deleted file mode 100644
index dc3dc3f..0000000
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diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLNet-MSELoss-3.pickle b/training_data/QL-M-G08-WW00-FSF10-DQLNet-MSELoss-3.pickle
deleted file mode 100644
index c5ab6c3..0000000
--- a/training_data/QL-M-G08-WW00-FSF10-DQLNet-MSELoss-3.pickle
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7b1a7eba046c33126ad9547d7f408a5b752b0c647650c53e51147da37d125185
-size 1316
diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLNet-MSELoss-3.torch b/training_data/QL-M-G08-WW00-FSF10-DQLNet-MSELoss-3.torch
deleted file mode 100644
index 96838bd..0000000
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diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-1.pickle b/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-1.pickle
deleted file mode 100644
index 406620d..0000000
--- a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-1.pickle
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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-size 505
diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-1.torch b/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-1.torch
deleted file mode 100644
index e2bd9ee..0000000
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diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-10.pickle b/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-10.pickle
deleted file mode 100644
index beb8159..0000000
--- a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-10.pickle
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-10.torch b/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-10.torch
deleted file mode 100644
index 745f047..0000000
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diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-11.pickle b/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-11.pickle
deleted file mode 100644
index 6133845..0000000
--- a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-11.pickle
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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-size 4868
diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-11.torch b/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-11.torch
deleted file mode 100644
index acb9e4d..0000000
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diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-12.pickle b/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-12.pickle
deleted file mode 100644
index b4fb427..0000000
--- a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-12.pickle
+++ /dev/null
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-size 5321
diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-12.torch b/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-12.torch
deleted file mode 100644
index e61d2d2..0000000
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diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-13.pickle b/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-13.pickle
deleted file mode 100644
index 62fafba..0000000
--- a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-13.pickle
+++ /dev/null
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-version https://git-lfs.github.com/spec/v1
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-size 5774
diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-13.torch b/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-13.torch
deleted file mode 100644
index 0fb8852..0000000
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diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-14.pickle b/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-14.pickle
deleted file mode 100644
index 3ddea9e..0000000
--- a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-14.pickle
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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-size 6227
diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-14.torch b/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-14.torch
deleted file mode 100644
index 6f02654..0000000
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diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-15.pickle b/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-15.pickle
deleted file mode 100644
index c5d4525..0000000
--- a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-15.pickle
+++ /dev/null
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-size 6680
diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-15.torch b/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-15.torch
deleted file mode 100644
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diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-16.pickle b/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-16.pickle
deleted file mode 100644
index 7cade5a..0000000
--- a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-16.pickle
+++ /dev/null
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diff --git a/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-16.torch b/training_data/QL-M-G08-WW00-FSF10-DQLSimple-MSELoss-16.torch
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