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log_1680378118.2503676.log
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[taxi-dqn.py - <module>:22] :
Hey, Welcome Amitabh !
[taxi-dqn.py - <module>:28] : Observation space : Discrete(500)
[taxi-dqn.py - <module>:29] : Number of states : 500
[taxi-dqn.py - <module>:30] : Action space : Discrete(6)
[taxi-dqn.py - <module>:31] : Number of action space : 6
[taxi-dqn.py - build_compile_model:69] : <bound method Model.summary of <keras.engine.sequential.Sequential object at 0x000001B8C1A1DD30>>
[taxi-dqn.py - build_compile_model:69] : <bound method Model.summary of <keras.engine.sequential.Sequential object at 0x000001B8C816A160>>
[taxi-dqn.py - align_target_model:76] : Weights of the network : [array([[-0.00977975, -0.01529572, 0.03108782, ..., -0.00684439,
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[ 2.01367497e-01, -1.57538846e-01, -2.95091450e-01,
-2.13063627e-01, -2.74570882e-02, 8.94092321e-02],
[ 1.14653885e-01, -8.72596055e-02, -1.32213444e-01,
3.20219636e-01, -8.32356662e-02, 1.04095489e-01],
[-4.26478088e-02, -8.10971111e-02, 2.86256790e-01,
-2.59384811e-01, 1.04892612e-01, 2.44889677e-01],
[-2.62487799e-01, -1.72276929e-01, 2.52917528e-01,
-2.13405460e-01, 2.75512099e-01, 4.79535758e-02],
[-3.69034708e-02, 2.38804698e-01, -4.37527299e-02,
5.27193546e-02, 2.12266624e-01, -1.15113765e-01],
[ 1.76024199e-01, 1.13209248e-01, 1.53263241e-01,
2.90004969e-01, 1.81650758e-01, 1.50351465e-01],
[-2.06873506e-01, 3.17242503e-01, 4.15063202e-02,
-1.96518436e-01, -2.00827852e-01, 2.04342723e-01],
[ 1.26359284e-01, -5.82251549e-02, -2.47109085e-01,
1.23296261e-01, 7.77150691e-02, -1.35924354e-01],
[ 3.23721170e-01, -1.61323756e-01, 2.35047042e-01,
-2.50202149e-01, -2.35567942e-01, -9.68054682e-02],
[-2.61630118e-02, -1.45340294e-01, 3.14773262e-01,
3.27219784e-01, -1.08139887e-01, -1.48265734e-01],
[ 7.68077672e-02, -1.54264659e-01, -5.41750789e-02,
1.64218247e-02, 1.44831389e-01, -1.38351426e-01],
[-7.29826689e-02, -2.13165864e-01, 3.15001011e-01,
2.11749017e-01, -1.84951544e-01, 1.50676876e-01],
[-2.06140876e-01, -2.56527662e-01, 2.69783139e-02,
-1.20857030e-01, 5.05762994e-02, 4.44684327e-02],
[ 1.33567214e-01, 2.63755977e-01, 3.11856747e-01,
-1.85870945e-01, -1.85045585e-01, 1.44525558e-01],
[ 7.99953341e-02, -2.57760286e-04, -2.66032338e-01,
2.67939866e-01, -2.99390167e-01, -2.45877370e-01],
[ 1.54741794e-01, -2.60512531e-02, -1.23255298e-01,
-6.35685325e-02, 1.98363364e-02, -7.96962827e-02],
[-2.21693307e-01, 9.89284217e-02, 1.24303162e-01,
-2.98175156e-01, 1.95885122e-01, 6.74358308e-02],
[-2.30323762e-01, 3.22766960e-01, 4.04535532e-02,
5.77162504e-02, -7.20605254e-03, 2.21050858e-01],
[-6.70163631e-02, 1.88702881e-01, -2.27240920e-01,
-2.52955347e-01, -2.47435153e-01, 2.38150954e-02],
[-1.99349135e-01, -2.94122964e-01, -4.91155982e-02,
1.07711196e-01, 2.74691761e-01, -1.82741344e-01],
[-3.38057280e-02, -1.87686875e-01, 2.00733721e-01,
-4.00295854e-03, -8.70621651e-02, -2.25805908e-01],
[ 2.74204850e-01, -1.75327778e-01, -1.91495106e-01,
3.52342427e-02, -2.70727187e-01, -2.85800934e-01],
[ 2.73904502e-01, -1.70318112e-01, -2.83138663e-01,
-1.65065572e-01, 2.86679208e-01, -9.41437334e-02],
[ 6.79423213e-02, -3.01035345e-01, 1.46743476e-01,
3.02724302e-01, -1.71869725e-01, 4.26313579e-02],
[ 1.85101926e-02, 1.03299469e-01, -3.18195134e-01,
1.49043798e-01, -2.35113278e-01, -5.85270822e-02],
[ 1.09777182e-01, 2.41358101e-01, 1.23792052e-01,
-2.57114470e-01, 5.89460135e-02, 2.82811880e-01],
[-1.81967035e-01, -2.49938220e-01, -1.98157758e-01,
-1.78141296e-02, 3.23074102e-01, 3.86321545e-02],
[ 2.73474455e-02, 1.88552141e-02, -6.29622340e-02,
2.19235778e-01, -2.55348623e-01, -1.10145852e-01],
[-1.01246461e-01, 5.84591925e-02, -3.24330151e-01,
-9.09184664e-02, 1.06718361e-01, -2.26240516e-01],
[ 1.50048018e-01, -1.60921693e-01, -2.63811529e-01,
1.40388519e-01, -2.74529040e-01, 6.52220547e-02],
[ 1.93952441e-01, -2.72010565e-02, -7.25969076e-02,
2.41947651e-01, -4.60261106e-03, 2.49642372e-01],
[-3.23134720e-01, 1.42092943e-01, -6.97392821e-02,
-2.51749098e-01, 1.51469320e-01, 1.22991979e-01],
[ 8.93899500e-02, -2.55385458e-01, -2.18789876e-01,
1.36754483e-01, 1.50554746e-01, 1.52190864e-01],
[-2.30831727e-01, 1.26173466e-01, 2.13173687e-01,
4.29563224e-02, 2.10344374e-01, -6.52756691e-02],
[ 1.29541010e-01, 1.23342872e-01, -3.69096994e-02,
-1.75958514e-01, 2.44089603e-01, -2.05364585e-01],
[-2.34384537e-01, -2.58773267e-02, 1.86994731e-01,
-3.04637849e-02, 1.14388287e-01, -3.65234911e-02],
[ 2.79523075e-01, 2.58445442e-01, -2.13258356e-01,
1.77176833e-01, 2.37955272e-01, 5.65180779e-02],
[ 2.97494531e-02, -8.71402770e-02, 1.75061822e-02,
-1.24197632e-01, 1.07947350e-01, -2.80111134e-02],
[ 1.84970140e-01, 4.61658835e-02, 1.56048119e-01,
2.94762850e-01, -6.20618761e-02, -2.87227839e-01]], dtype=float32), array([0., 0., 0., 0., 0., 0.], dtype=float32)]
[functional.py - _conform_to_reference_input:678] : Tensore receied : Tensor("IteratorGetNext:0", shape=(None, 1), dtype=int32)
[functional.py - _conform_to_reference_input:679] : REf input receied : KerasTensor(type_spec=TensorSpec(shape=(None, 1), dtype=tf.float32, name='embedding_input'), name='embedding_input', description="created by layer 'embedding_input'")
[taxi-dqn.py - <module>:144] : **********************************
[taxi-dqn.py - <module>:145] : Episode: 1 => Total Reward : -1378
[taxi-dqn.py - <module>:147] : **********************************
[taxi-dqn.py - <module>:144] : **********************************
[taxi-dqn.py - <module>:145] : Episode: 2 => Total Reward : -2818
[taxi-dqn.py - <module>:147] : **********************************
[taxi-dqn.py - <module>:144] : **********************************
[taxi-dqn.py - <module>:145] : Episode: 3 => Total Reward : -4879
[taxi-dqn.py - <module>:147] : **********************************
[taxi-dqn.py - <module>:144] : **********************************
[taxi-dqn.py - <module>:145] : Episode: 4 => Total Reward : -1333
[taxi-dqn.py - <module>:147] : **********************************
[taxi-dqn.py - <module>:144] : **********************************
[taxi-dqn.py - <module>:145] : Episode: 5 => Total Reward : -8245
[taxi-dqn.py - <module>:147] : **********************************
[taxi-dqn.py - <module>:144] : **********************************
[taxi-dqn.py - <module>:145] : Episode: 6 => Total Reward : -6256
[taxi-dqn.py - <module>:147] : **********************************
[taxi-dqn.py - <module>:144] : **********************************
[taxi-dqn.py - <module>:145] : Episode: 7 => Total Reward : -1297
[taxi-dqn.py - <module>:147] : **********************************
[taxi-dqn.py - <module>:144] : **********************************
[taxi-dqn.py - <module>:145] : Episode: 8 => Total Reward : -1549
[taxi-dqn.py - <module>:147] : **********************************
[taxi-dqn.py - <module>:144] : **********************************
[taxi-dqn.py - <module>:145] : Episode: 9 => Total Reward : -1171
[taxi-dqn.py - <module>:147] : **********************************
[taxi-dqn.py - <module>:144] : **********************************
[taxi-dqn.py - <module>:145] : Episode: 10 => Total Reward : -1351
[taxi-dqn.py - <module>:147] : **********************************