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Last active January 23, 2017 11:52
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INFO:trl.experiment: Initialized env <CarOnHill instance>
INFO:trl.experiment: observation space: Box(2,)
INFO:trl.experiment: action space: DiscreteValued(<built-in method tolist of numpy.ndarray object at 0x7faacc7818a0>)
INFO:trl.experiment: Random seeds (np, env): 2696744048250742569 None
INFO:trl.experiment: Discretized actions (2): [-4. 4.]
INFO:trl.experiment: Gamma: 0.950000
INFO:trl.experiment: Loading dataset from car.h5
INFO:trl.experiment: Collected 61443 samples
Using TensorFlow backend.
INFO:trl.regressor: Loaded regressor from car.h5
INFO:trl.experiment: Skipping training.
INFO:trl.experiment: Evaluating algorithm (episodes: 1)
INFO:trl.evaluation: Episode 0 starting in [-1. -3.]
DEBUG:trl.evaluation: (array([-1., -3.]), -4.0, 0.0, array([-0.51966909, -0.3866922 ]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.51966909, -0.3866922 ]), -4.0, 0.0, array([-0.57458171, -0.69058505]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.57458171, -0.69058505]), -4.0, 0.0, array([-0.65242364, -0.83584272]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.65242364, -0.83584272]), -4.0, 0.0, array([-0.7358447 , -0.80664201]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.7358447 , -0.80664201]), -4.0, 0.0, array([-0.80970714, -0.6561604 ]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.80970714, -0.6561604 ]), -4.0, 0.0, array([-0.86512874, -0.44623256]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.86512874, -0.44623256]), 4.0, 0.0, array([-0.8730663 , 0.28545492]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.8730663 , 0.28545492]), -4.0, 0.0, array([-0.8337455 , 0.49735218]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.8337455 , 0.49735218]), -4.0, 0.0, array([-0.77477713, 0.67307976]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.77477713, 0.67307976]), -4.0, 0.0, array([-0.70175567, 0.769424 ]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.70175567, 0.769424 ]), -4.0, 0.0, array([-0.625424 , 0.73084559]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.625424 , 0.73084559]), -4.0, 0.0, array([-0.5609914 , 0.53265687]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.5609914 , 0.53265687]), -4.0, 0.0, array([-0.52292847, 0.21451287]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.52292847, 0.21451287]), -4.0, 0.0, array([-0.51960236, -0.14914752]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.51960236, -0.14914752]), -4.0, 0.0, array([-0.55171589, -0.48142768]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.55171589, -0.48142768]), -4.0, 0.0, array([-0.61227963, -0.70621713]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.61227963, -0.70621713]), -4.0, 0.0, array([-0.68764086, -0.77392987]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.68764086, -0.77392987]), -4.0, 0.0, array([-0.76223782, -0.69824658]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.76223782, -0.69824658]), -4.0, 0.0, array([-0.82432398, -0.53314729]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.82432398, -0.53314729]), -4.0, 0.0, array([-0.86747965, -0.32564498]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.86747965, -0.32564498]), -4.0, 0.0, array([-0.88892352, -0.10180988]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.88892352, -0.10180988]), -4.0, 0.0, array([-0.88771869, 0.12583789]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.88771869, 0.12583789]), -4.0, 0.0, array([-0.8639107 , 0.34868777]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.8639107 , 0.34868777]), -4.0, 0.0, array([-0.8185739 , 0.55326707]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.8185739 , 0.55326707]), -4.0, 0.0, array([-0.75477613, 0.71149156]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.75477613, 0.71149156]), -4.0, 0.0, array([-0.67944558, 0.77431314]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.67944558, 0.77431314]), -4.0, 0.0, array([-0.60489167, 0.6894554 ]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.60489167, 0.6894554 ]), -4.0, 0.0, array([-0.54678507, 0.45006818]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.54678507, 0.45006818]), -4.0, 0.0, array([-0.51822637, 0.11079832]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.51822637, 0.11079832]), -4.0, 0.0, array([-0.52539628, -0.25169799]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.52539628, -0.25169799]), -4.0, 0.0, array([-0.56677782, -0.5604711 ]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.56677782, -0.5604711 ]), -4.0, 0.0, array([-0.63322345, -0.74257294]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.63322345, -0.74257294]), -4.0, 0.0, array([-0.70987604, -0.76469434]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.70987604, -0.76469434]), -4.0, 0.0, array([-0.78181866, -0.65729766]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.78181866, -0.65729766]), -4.0, 0.0, array([-0.8388976 , -0.47609038]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.8388976 , -0.47609038]), -4.0, 0.0, array([-0.87596369, -0.26199078]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.87596369, -0.26199078]), -4.0, 0.0, array([-0.89091461, -0.03610984]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.89091461, -0.03610984]), -4.0, 0.0, array([-0.8831434 , 0.19108319]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.8831434 , 0.19108319]), -4.0, 0.0, array([-0.8529494 , 0.41049114]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.8529494 , 0.41049114]), -4.0, 0.0, array([-0.80183962, 0.60547165]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.80183962, 0.60547165]), -4.0, 0.0, array([-0.73377673, 0.74201603]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.73377673, 0.74201603]), -4.0, 0.0, array([-0.65717415, 0.76657577]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.65717415, 0.76657577]), -4.0, 0.0, array([-0.58574844, 0.6347252 ]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.58574844, 0.6347252 ]), -4.0, 0.0, array([-0.53508887, 0.35905262]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.53508887, 0.35905262]), -4.0, 0.0, array([-0.51655017, 0.00516995]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.51655017, 0.00516995]), -4.0, 0.0, array([-0.53409066, -0.34979157]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.53409066, -0.34979157]), -4.0, 0.0, array([-0.58396925, -0.62870359]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.58396925, -0.62870359]), -4.0, 0.0, array([-0.6550172 , -0.76510989]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.6550172 , -0.76510989]), -4.0, 0.0, array([-0.73168337, -0.74451711]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.73168337, -0.74451711]), -4.0, 0.0, array([-0.80012751, -0.61033029]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.80012751, -0.61033029]), -4.0, 0.0, array([-0.85178493, -0.41642717]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.85178493, -0.41642717]), -4.0, 0.0, array([-0.88259631, -0.19742081]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.88259631, -0.19742081]), -4.0, 0.0, array([-0.89100729, 0.02969136]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.89100729, 0.02969136]), -4.0, 0.0, array([-0.87669276, 0.25572614]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.87669276, 0.25572614]), -4.0, 0.0, array([-0.84023043, 0.4703728 ]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.84023043, 0.4703728 ]), -4.0, 0.0, array([-0.78366378, 0.65295975]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.78366378, 0.65295975]), -4.0, 0.0, array([-0.71202761, 0.76318505]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.71202761, 0.76318505]), -4.0, 0.0, array([-0.63531878, 0.74537951]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.63531878, 0.74537951]), -4.0, 0.0, array([-0.56836641, 0.56762999]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.56836641, 0.56762999]), -4.0, 0.0, array([-0.52611894, 0.26149527]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.52611894, 0.26149527]), -4.0, 0.0, array([-0.51792872, -0.10054721]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.51792872, -0.10054721]), -4.0, 0.0, array([-0.54552948, -0.44152928]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.54552948, -0.44152928]), -4.0, 0.0, array([-0.60295656, -0.68469103]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.60295656, -0.68469103]), -4.0, 0.0, array([-0.67726506, -0.77413074]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.67726506, -0.77413074]), -4.0, 0.0, array([-0.75276759, -0.71484026]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.75276759, -0.71484026]), -4.0, 0.0, array([-0.81700829, -0.55853877]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.81700829, -0.55853877]), -4.0, 0.0, array([-0.86292011, -0.35478774]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.86292011, -0.35478774]), -4.0, 0.0, array([-0.88735533, -0.13222332]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.88735533, -0.13222332]), -4.0, 0.0, array([-0.88920127, 0.09540956]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.88920127, 0.09540956]), -4.0, 0.0, array([-0.86838814, 0.31948427]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.86838814, 0.31948427]), -4.0, 0.0, array([-0.82581791, 0.52771577]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.82581791, 0.52771577]), -4.0, 0.0, array([-0.76419919, 0.69455302]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.76419919, 0.69455302]), -4.0, 0.0, array([-0.68982003, 0.77354837]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.68982003, 0.77354837]), -4.0, 0.0, array([-0.61427446, 0.71037016]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.61427446, 0.71037016]), -4.0, 0.0, array([-0.55308322, 0.48956945]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.55308322, 0.48956945]), -4.0, 0.0, array([-0.52003666, 0.15929687]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.52003666, 0.15929687]), -4.0, 0.0, array([-0.52233831, -0.20452957]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.52233831, -0.20452957]), -4.0, 0.0, array([-0.55950192, -0.52502123]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.55950192, -0.52502123]), -4.0, 0.0, array([-0.62337045, -0.72741177]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.62337045, -0.72741177]), -4.0, 0.0, array([-0.69958725, -0.77042605]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.69958725, -0.77042605]), -4.0, 0.0, array([-0.77287579, -0.67713516]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.77287579, -0.67713516]), -4.0, 0.0, array([-0.83233693, -0.50294649]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.83233693, -0.50294649]), -4.0, 0.0, array([-0.87225366, -0.29167573]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.87225366, -0.29167573]), -4.0, 0.0, array([-0.89022619, -0.0666346 ]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.89022619, -0.0666346 ]), -4.0, 0.0, array([-0.88550174, 0.16085334]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.88550174, 0.16085334]), -4.0, 0.0, array([-0.85826002, 0.38200586]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.85826002, 0.38200586]), -4.0, 0.0, array([-0.80979577, 0.58175757]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.80979577, 0.58175757]), -4.0, 0.0, array([-0.74363493, 0.72891894]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.74363493, 0.72891894]), -4.0, 0.0, array([-0.66748531, 0.77180143]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.66748531, 0.77180143]), -4.0, 0.0, array([-0.59443846, 0.6617421 ]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.59443846, 0.6617421 ]), -4.0, 0.0, array([-0.5401915 , 0.40223568]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.5401915 , 0.40223568]), -4.0, 0.0, array([-0.51694887, 0.05432901]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.51694887, 0.05432901]), -4.0, 0.0, array([-0.52970217, -0.30492683]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.52970217, -0.30492683]), -4.0, 0.0, array([-0.57574356, -0.59847115]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.57574356, -0.59847115]), -4.0, 0.0, array([-0.6448192 , -0.75635932]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.6448192 , -0.75635932]), -4.0, 0.0, array([-0.72163238, -0.75515575]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.72163238, -0.75515575]), -4.0, 0.0, array([-0.79179666, -0.63280369]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.79179666, -0.63280369]), -4.0, 0.0, array([-0.84601644, -0.44440674]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.84601644, -0.44440674]), -4.0, 0.0, array([-0.8797491 , -0.22749153]), 0.0, 0.0)
DEBUG:trl.evaluation: (array([-0.8797491 , -0.22749153]), -4.0, 0.0, array([ -8.91200617e-01, -8.57585915e-04]), 0.0, 0.0)
INFO:trl.evaluation: Episode 0 finished in 100 steps.
INFO:trl.evaluation: Metrics: (0.0, 0.0)
INFO:trl.experiment: Evaluating algorithm (episodes: 289)
INFO:trl.evaluation: Episode 0 starting in [-1. -3.]
INFO:trl.evaluation: Episode 0 finished in 20 steps.
INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 1 starting in [-1. -2.625]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 2 starting in [-1. -2.25]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 3 starting in [-1. -1.875]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 4 starting in [-1. -1.5]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 5 starting in [-1. -1.125]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 6 starting in [-1. -0.75]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 7 starting in [-1. -0.375]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 9 starting in [-1. 0.375]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 10 starting in [-1. 0.75]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 11 starting in [-1. 1.125]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 12 starting in [-1. 1.5]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 13 starting in [-1. 1.875]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 14 starting in [-1. 2.25]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 15 starting in [-1. 2.625]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 16 starting in [-1. 3.]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 17 starting in [-0.875 -3. ]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 18 starting in [-0.875 -2.625]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 19 starting in [-0.875 -2.25 ]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 20 starting in [-0.875 -1.875]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 21 starting in [-0.875 -1.5 ]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 22 starting in [-0.875 -1.125]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 23 starting in [-0.875 -0.75 ]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 24 starting in [-0.875 -0.375]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 25 starting in [-0.875 0. ]
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INFO:trl.evaluation: Metrics: (0.050000000000000003, 0.37735360253530725)
INFO:trl.evaluation: Episode 26 starting in [-0.875 0.375]
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./run.py CarOnHill-v0 fqi -q nn2 --training-iterations 100 --training-episodes 1100
INFO:trl.experiment: Random seeds (np, env): 5998303908528108434 None
INFO:trl.experiment: Initialized env CarOnHill-v0
INFO:trl.experiment: observation space: Box(2,)
INFO:trl.experiment: action space: DiscreteValued(<built-in method tolist of numpy.ndarray object at 0x7fb7c0da4da0>)
INFO:trl.experiment: Discretized actions (2): [-4. 4.]
INFO:trl.experiment: Gamma: 0.950000
INFO:trl.experiment: Collecting training data (episodes: 1100, horizon: 100)
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INFO:trl.experiment: Collected 61770 samples
Using TensorFlow backend.
INFO:trl.experiment: Training algorithm (iterations: 100, budget: None)
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INFO:trl.experiment: Evaluating algorithm (episodes: 289)
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