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step: 0.75 s) +episode steps: 1059 +episode reward: -500.08 +episode max | avg | min reward: 6.24 | -0.47 | -8.85 +EPSILON = 0.480000 -Episode 3 ==> CRASH {0.22,-2.45,-119.53} 23/01/2023 05:14:04 -episode time: 66.99 s (avg step: 0.51 s) -episode steps: 132 -episode reward: -108.90 -episode max | avg | min reward: 0.40 | -0.83 | -100.00 -T = 22.562500 +TOO BIG STEP TIME: 2.47 s +----------- ---------------------------------------- +episode time: 488.49 s (avg step: 0.72 s) +episode steps: 676 +episode reward: -502.61 +episode max | avg | min reward: 6.37 | -0.74 | -8.84 +EPSILON = 0.460800 -Episode 4 ==> CRASH {-1.86,-1.65,-114.89} 23/01/2023 05:14:30 -episode time: 25.53 s (avg step: 0.52 s) -episode steps: 49 -episode reward: -105.30 -episode max | avg | min reward: 0.40 | -2.19 | -100.00 -T = 21.434375 +TOO BIG STEP TIME: 2.32 s diff --git a/Data/Log_learning/Qtable.csv b/Data/Log_learning/Qtable.csv index 3f334d7..7a95e68 100644 --- a/Data/Log_learning/Qtable.csv +++ 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a/Data/Log_learning_ALPHA_0dot7/reward_min_per_episode.csv b/Data/Log_learning_ALPHA_0dot7/reward_min_per_episode.csv index 6333a95..a3fde6b 100644 --- a/Data/Log_learning_ALPHA_0dot7/reward_min_per_episode.csv +++ b/Data/Log_learning_ALPHA_0dot7/reward_min_per_episode.csv @@ -1,200 +1,200 @@ --1.000000000000000000e+02 --1.000000000000000000e+02 --3.000000000000000444e-01 --1.000000000000000000e+02 --1.000000000000000000e+02 --1.000000000000000000e+02 --1.000000000000000000e+02 --1.000000000000000000e+02 --1.000000000000000000e+02 --3.000000000000000444e-01 --1.000000000000000000e+02 --1.000000000000000000e+02 --1.000000000000000000e+02 --1.000000000000000000e+02 --1.000000000000000000e+02 --1.000000000000000000e+02 --1.000000000000000000e+02 --1.000000000000000000e+02 --1.000000000000000000e+02 --1.000000000000000000e+02 --3.000000000000000444e-01 --1.000000000000000000e+02 --3.000000000000000444e-01 --1.000000000000000000e+02 --1.000000000000000000e+02 --1.000000000000000000e+02 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b/Data/Log_learning_RANDOM/reward_avg_per_episode.csv index 4528f34..019c4f9 100644 --- a/Data/Log_learning_RANDOM/reward_avg_per_episode.csv +++ b/Data/Log_learning_RANDOM/reward_avg_per_episode.csv @@ -1,1000 +1,1000 @@ --2.213043478260869446e+00 --5.722222222222222321e+00 --8.278688524590164244e-01 --4.971428571428571530e+00 --1.856896551724138034e+00 --5.788888888888888751e+00 --4.015384615384615330e+00 --2.863888888888888928e+00 --2.594999999999999751e+00 --6.666666666666666297e-01 --5.952941176470588402e+00 --7.370629370629371291e-01 --8.061068702290076216e-01 --4.329166666666666607e+00 --4.976190476190476275e+00 --5.688888888888889106e+00 --6.025000000000000355e-01 --7.399999999999999467e+00 --1.263218390804597746e+00 --3.328124999999999556e+00 --4.391666666666666607e+00 --3.501538461538462044e-01 --6.431693989071038953e-01 --2.376744186046511498e+00 --1.312195121951219345e+00 --4.582995951417004887e-01 --4.695454545454545325e+00 --9.050847457627118064e-01 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--3.206412825651309105e-03 -1.134268537074148292e-01 -1.154308617234468953e-01 --8.727272727272727959e-01 -6.472945891783567218e-02 -4.008016032064127404e-02 -2.885771543086171689e-02 -2.505010020040079888e-02 -4.288577154308617634e-02 -5.691382765531061844e-02 -4.268537074148295862e-02 --1.743486973947895938e-02 --1.048360655737704761e+00 --8.016032064128441898e-04 -5.891783567134268457e-02 -3.727454909819637868e-02 -6.412825651302604679e-02 -4.288577154308616940e-02 -2.464929859719438079e-02 --1.002004008016037402e-03 -1.030060120240480909e-01 --2.004008016032074804e-03 -6.893787575150300828e-02 -1.082164328657313560e-02 -2.665330661322644346e-02 -7.955911823647293657e-02 -4.689378757515028778e-02 -4.448897795591181398e-02 --4.371527777777777790e-01 -2.064128256513025547e-02 -2.344689378757514389e-02 -6.472945891783567218e-02 -9.358717434869739948e-02 -3.346693386773546414e-02 -4.929859719438877547e-02 -8.476953907815631961e-02 --1.402805611222454184e-03 -5.671342685370740766e-02 --4.425373134328358660e-01 --3.934615384615384404e+00 -6.593186372745490909e-02 -2.024048096192384086e-02 --3.206412825651319513e-03 -2.765531062124247999e-02 -5.090180360721442698e-02 --4.154574132492114269e-01 -3.486973947895791875e-02 -7.835671342685369967e-02 -2.805611222444889113e-02 -4.168336673346693250e-02 -1.026052104208416832e-01 -6.412825651302603291e-02 -3.426853707414829336e-02 -2.745490981963926574e-02 -6.412825651302604679e-02 -6.072144288577153298e-02 -5.330661322645290079e-02 -5.090180360721442698e-02 --1.082164328657315641e-02 -6.753507014028056754e-02 -7.034068136272544902e-02 --6.573146292585173300e-02 -4.989979959919839392e-02 -6.332665330661321756e-02 -4.348697394789578091e-02 -5.390781563126250536e-02 -1.054108216432865647e-01 -1.943887775551101163e-02 -9.098196392785570796e-02 --4.349056603773585050e-01 -8.076152304609217347e-02 -7.915831663326652889e-02 -4.629258517034066933e-02 -6.773547094188377138e-02 --3.206412825651312574e-03 -6.212424849699390954e-03 -5.310621242484969695e-02 --4.393333333333333535e-01 -4.909819639278557163e-02 -3.246492985971943107e-02 -3.286573146292583875e-02 -4.649298597194388011e-02 -7.555110220440880431e-02 -5.450901803607213769e-02 -4.949899799599197237e-02 -1.122244488977954847e-02 -4.609218436873747243e-02 -4.689378757515029472e-02 --4.008016032064220949e-04 -7.635270541082163354e-02 --5.399999999999999467e+00 -3.847695390781562946e-02 --2.004008016032110475e-04 --1.721442885771543030e-01 -2.745490981963926574e-02 -2.805611222444889113e-02 -2.725450901803606885e-02 -3.767535070140280717e-02 -6.873747494989979057e-02 -1.723446893787574860e-02 -1.603206412825650476e-02 --3.507014028056113647e-02 -1.006012024048096309e-01 -2.565130260521040692e-02 --1.165999999999999925e+00 -2.164328657314628854e-02 -1.162324649298596309e-02 -3.567134268537073410e-02 --2.925851703406814538e-02 -8.677354709418838574e-02 -5.851703406813626301e-02 -3.527054108216432643e-02 --6.465000000000000746e-01 -3.947895791583165559e-02 --1.026126126126126081e+00 --2.060796645702306085e-01 --9.053030303030302761e-01 -4.989979959919839392e-02 -1.703406813627253436e-02 -3.567134268537073410e-02 --2.457286432160804002e-01 -3.306613226452905646e-02 -1.062124248496992308e-02 --2.896341463414633943e-01 --1.002004008016033065e-02 -4.288577154308616940e-02 -3.787575150300600407e-02 --6.833667334669341065e-02 -7.995991983967935812e-02 -3.206412825651302600e-03 --8.617234468937882974e-03 -1.843687374749498203e-02 -5.090180360721442004e-02 --1.541082164328657356e-01 --1.062124248496993002e-02 -1.342685370741481497e-02 -3.466933867735470104e-02 -2.344689378757514042e-02 --8.416833667334675667e-03 -3.827655310621241869e-02 -6.873747494989979057e-02 --2.502380952380952372e+00 -4.068136272545088555e-02 -4.809619238476937203e-03 --4.144000000000000128e+00 --1.002004008016033412e-02 -5.050100200400800543e-02 -3.907815631262523404e-02 -3.186372745490981956e-02 -4.188376753507013633e-02 --3.598746081504702321e-01 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-4.719999999999995310e+01 -3.839999999999994884e+01 -4.949999999999995737e+01 -3.219999999999998153e+01 --6.039999999999937330e+01 -4.990000000000000568e+01 -7.899999999999987033e+00 -3.839999999999993463e+01 -3.029999999999996874e+01 -3.409999999999995879e+01 -4.489999999999997016e+01 -4.880000000000002558e+01 -3.809999999999991616e+01 --1.087999999999999972e+02 -3.439999999999997726e+01 -5.280000000000001137e+01 -5.789999999999994174e+01 --1.030000000000000000e+02 -8.899999999999996803e+00 -5.720000000000003837e+01 -1.869999999999996732e+01 -3.639999999999994174e+01 -3.869999999999986784e+01 -4.949999999999992184e+01 --1.032999999999999972e+02 --1.000000000000015543e+00 --1.045000000000000000e+02 -2.559999999999996234e+01 --1.424999999999999716e+02 --1.150000000000006395e+01 --4.709999999999983800e+01 -1.090000000000000391e+01 -4.050000000000003553e+01 --8.090000000000004832e+01 -1.549999999999993960e+01 -3.339999999999988489e+01 -4.699999999999987743e+00 -2.489999999999994529e+01 -2.839999999999998082e+01 -4.720000000000008811e+01 --1.072000000000000171e+02 -5.640000000000006253e+01 -3.930000000000006111e+01 -6.239999999999997016e+01 --8.300000000000004263e+01 -1.239999999999993641e+01 --7.499999999999954525e+01 --5.819999999999960494e+01 --1.015999999999999943e+02 -2.579999999999995097e+01 -9.099999999999994316e+00 -3.789999999999993463e+01 -5.360000000000005826e+01 -3.399999999999988631e+01 -3.349999999999992895e+01 -4.199999999999992184e+01 --1.087999999999999972e+02 -4.510000000000012221e+01 -5.340000000000005542e+01 --7.170000000000001705e+01 -3.320000000000008811e+01 --1.132000000000000171e+02 -2.269999999999994600e+01 -3.619999999999988916e+01 -3.629999999999993321e+01 -2.300000000000001066e+01 -3.439999999999992752e+01 --1.102999999999999972e+02 -2.519999999999994245e+01 -1.839999999999990266e+01 --1.153000000000000256e+02 --9.850000000000000000e+01 -3.109999999999992681e+01 -2.399999999999993250e+01 --1.338000000000000114e+02 --8.890000000000003411e+01 -2.929999999999990834e+01 -6.089999999999991331e+01 -3.609999999999990905e+01 --3.330000000000004690e+01 --5.620000000000000995e+01 --8.320000000000003126e+01 --1.105000000000000000e+02 -4.369999999999998863e+01 --8.610000000000005116e+01 -2.059999999999993037e+01 -5.390000000000002700e+01 -4.920000000000001705e+01 --1.102999999999999972e+02 -4.560000000000007248e+01 -6.759999999999998010e+01 -3.979999999999996874e+01 --1.310000000000005294e+01 -1.199999999999997691e+01 -2.390000000000003055e+01 -1.859999999999994813e+01 --1.300000000000018696e+00 -7.199999999999929123e+00 -8.899999999999938183e+00 --9.340000000000004832e+01 --1.502999999999998408e+02 -7.999999999999525269e-01 --1.067999999999999972e+02 -3.289999999999992752e+01 -3.170000000000004903e+01 --1.009000000000000057e+02 --7.860000000000003695e+01 -3.959999999999994458e+01 -1.569999999999994600e+01 -2.069999999999997797e+01 -3.829999999999996874e+01 -3.619999999999998863e+01 --5.999999999999873213e-01 -1.829999999999996163e+01 -3.860000000000000853e+01 -2.559999999999993747e+01 --6.709999999999996589e+01 --9.959999999999999432e+01 --1.168000000000000114e+02 -5.199999999999992184e+01 -2.839999999999994884e+01 -3.309999999999994458e+01 -5.399999999999998579e+01 -3.129999999999990479e+01 --9.759999999999999432e+01 -3.519999999999994600e+01 --1.194000000000000199e+02 --7.590000000000001990e+01 --7.339999999999959357e+01 -1.579999999999991189e+01 -1.859999999999996234e+01 -3.869999999999996021e+01 -1.279999999999999005e+01 --1.040000000000000000e+02 -5.399999999999967493e+00 --9.130000000000006821e+01 --9.300000000000007816e+00 -2.989999999999994174e+01 -3.530000000000009663e+01 -1.589999999999993463e+01 -2.969999999999995666e+01 -1.849999999999997158e+01 --1.115000000000000000e+02 -3.190000000000000924e+01 -4.070000000000001705e+01 --9.600000000000002842e+01 -1.029999999999999716e+01 --1.034999999999999858e+02 -1.789999999999993108e+01 --1.086000000000000085e+02 -1.570000000000000107e+01 --9.790000000000000568e+01 -1.139999999999992930e+01 -1.730000000000000426e+01 --8.970000000000001705e+01 -2.709999999999999432e+01 -2.579999999999993321e+01 --8.309999999999998010e+01 -1.600000000000000711e+01 -1.759999999999999787e+01 --7.970000000000003126e+01 --1.054000000000000057e+02 --1.015999999999999943e+02 -2.339999999999987068e+01 --1.126000000000000085e+02 --8.900000000000025224e+00 -2.539999999999999147e+01 -3.570000000000023022e+01 -1.929999999999997584e+01 --1.195000000000000284e+02 --1.130000000000000000e+02 -1.599999999999998934e+01 --1.350000000000005329e+01 -5.999999999999940492e+00 --8.000000000000446754e-01 -1.199999999999989875e+01 --7.539999999999953673e+01 --1.710000000000003340e+01 -2.419999999999994245e+01 -4.499999999999982236e+00 --2.100000000000004619e+01 -2.259999999999997300e+01 -3.849999999999998579e+01 -1.489999999999998970e+01 -4.120000000000007390e+01 -4.039999999999998437e+01 -1.549999999999985612e+01 --7.190000000000000568e+01 --9.550000000000001421e+01 -2.860000000000018616e+01 --1.038999999999999915e+02 -4.860000000000013642e+01 --1.090999999999999943e+02 -3.859999999999990195e+01 -8.399999999999968381e+00 -1.579999999999987104e+01 -4.590000000000005542e+01 -2.049999999999993960e+01 --1.097999999999999972e+02 -6.499999999999960920e+00 -3.819999999999989626e+01 -4.799999999999917222e+00 --1.032000000000000171e+02 -4.719999999999996021e+01 --1.682999999999995850e+02 -4.969999999999990337e+01 -2.079999999999991900e+01 --1.084000000000000057e+02 -3.839999999999994884e+01 --1.334000000000000341e+02 --7.469999999999957652e+01 -2.509999999999999076e+01 -9.300000000000006040e+00 -3.439999999999998437e+01 -1.789999999999997726e+01 -3.809999999999990195e+01 --4.729999999999973426e+01 -4.640000000000001279e+01 -3.399999999999992895e+01 -3.369999999999997442e+01 -4.419999999999998153e+01 -2.239999999999993463e+01 -2.539999999999990621e+01 -2.029999999999999005e+01 -3.099999999999979217e+00 -5.389999999999999858e+01 -2.399999999999994671e+01 -6.199999999999996625e+00 -3.309999999999995879e+01 -2.959999999999995168e+01 -2.169999999999997797e+01 -2.719999999999992824e+01 -3.699999999999995737e+01 -3.279999999999993321e+01 -2.939999999999998082e+01 -9.699999999999914024e+00 -3.139999999999994174e+01 --2.800000000000007105e+01 -1.259999999999989662e+01 -3.559999999999995879e+01 -3.109999999999990905e+01 -1.559999999999998366e+01 -1.249999999999998401e+01 -3.320000000000000995e+01 --1.469999999999998295e+02 -3.079999999999995097e+01 -1.319999999999999396e+01 -2.039999999999989555e+01 --1.092999999999999972e+02 --1.300000000000014477e+00 -1.709999999999994458e+01 -1.019999999999997442e+01 -2.439999999999992397e+01 -2.439999999999998082e+01 --1.299000000000000625e+02 -8.199999999999931788e+00 -5.000000000000017764e+00 -1.079999999999994742e+01 --1.600000000000038280e+00 -5.659999999999991616e+01 -5.759999999999990905e+01 --1.152000000000000171e+02 -3.229999999999996163e+01 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-1.380000000000001137e+01 -2.539999999999995239e+01 --1.317000000000000171e+02 -1.739999999999992042e+01 -3.909999999999998010e+01 -1.399999999999999467e+01 -2.079999999999990123e+01 -5.119999999999993179e+01 -3.199999999999986855e+01 -1.709999999999994813e+01 -1.370000000000001883e+01 -3.200000000000007816e+01 -3.030000000000003268e+01 -2.659999999999994813e+01 -2.539999999999996305e+01 --5.399999999999993250e+00 -3.369999999999994600e+01 -3.509999999999997300e+01 --3.280000000000010374e+01 -2.489999999999999147e+01 -3.159999999999993747e+01 -2.170000000000000639e+01 -2.690000000000006963e+01 -5.259999999999997300e+01 -9.699999999999889155e+00 -4.539999999999995595e+01 --9.220000000000001705e+01 -4.029999999999996874e+01 -3.949999999999995737e+01 -2.309999999999997300e+01 -3.379999999999996163e+01 --1.599999999999992317e+00 -3.099999999999959233e+00 -2.649999999999994671e+01 --1.318000000000000966e+02 -2.449999999999985789e+01 -1.619999999999999218e+01 -1.639999999999992397e+01 -2.319999999999988916e+01 -3.769999999999993179e+01 -2.720000000000003482e+01 -2.469999999999995310e+01 -5.599999999999997868e+00 -2.299999999999996092e+01 -2.339999999999994529e+01 --1.999999999999918510e-01 -3.809999999999990195e+01 --1.025999999999999943e+02 -1.919999999999995666e+01 --1.000000000000223488e-01 --8.589999999999943725e+01 -1.369999999999992824e+01 -1.399999999999993250e+01 -1.359999999999997478e+01 -1.879999999999997939e+01 -3.430000000000006821e+01 -8.599999999999946354e+00 -7.999999999999998224e+00 --1.750000000000002132e+01 -5.019999999999991047e+01 -1.279999999999994920e+01 --1.166000000000000227e+02 -1.079999999999998295e+01 -5.800000000000008704e+00 -1.779999999999998295e+01 --1.460000000000003162e+01 -4.329999999999997584e+01 -2.919999999999987850e+01 -1.759999999999989839e+01 --1.293000000000000114e+02 -1.969999999999990692e+01 --1.139000000000000199e+02 --9.830000000000006821e+01 --1.195000000000000284e+02 -2.489999999999993818e+01 -8.499999999999971578e+00 -1.779999999999990479e+01 --9.780000000000006821e+01 -1.649999999999998934e+01 -5.299999999999880806e+00 --9.500000000000005684e+01 --5.000000000000039080e+00 -2.139999999999995950e+01 -1.889999999999998082e+01 --3.409999999999993037e+01 -3.989999999999993463e+01 -1.599999999999998312e+00 --4.300000000000000711e+00 -9.199999999999956657e+00 -2.539999999999995950e+01 --7.689999999999959357e+01 --5.300000000000020250e+00 -6.699999999999985079e+00 -1.729999999999997229e+01 -1.169999999999996554e+01 --4.200000000000065015e+00 -1.909999999999994458e+01 -3.430000000000011795e+01 --1.050999999999999943e+02 -2.029999999999991189e+01 -2.399999999999965716e+00 --1.035999999999999943e+02 --5.000000000000061284e+00 -2.520000000000009521e+01 -1.949999999999997868e+01 -1.589999999999995417e+01 -2.089999999999998082e+01 --1.148000000000000256e+02 --1.052999999999999972e+02 --1.058999999999999915e+02 -2.269999999999994245e+01 -1.899999999999977707e+00 -3.439999999999994174e+01 -9.999999999999970024e-01 -2.879999999999993321e+01 --1.029000000000000057e+02 -3.600000000000007105e+01 --4.900000000000042988e+00 -2.399999999999995026e+00 -2.369999999999994955e+01 -4.749999999999987921e+01 -9.899999999999948841e+00 -1.509999999999994102e+01 -1.409999999999999076e+01 -2.299999999999995026e+01 -2.269999999999995666e+01 -1.739999999999997016e+01 -1.269999999999989448e+01 --1.780000000000002913e+01 --1.052000000000000028e+02 -2.429999999999993321e+01 -2.129999999999995808e+01 -2.979999999999989413e+01 --1.441999999999999602e+02 -2.489999999999993463e+01 -1.109999999999997300e+01 --1.050000000000004796e+01 --9.160000000000006537e+01 -3.579999999999994031e+01 --1.030000000000000000e+02 -5.170000000000006679e+01 --1.035000000000000000e+02 -4.679999999999999005e+01 -2.489999999999997371e+01 --1.430000000000007176e+01 -7.999999999999932498e+00 -3.159999999999988773e+01 --8.450000000000004263e+01 -3.299999999999995026e+01 -3.990000000000004121e+01 --1.175000000000000284e+02 -1.859999999999996234e+01 --1.173000000000000398e+02 -2.009999999999997300e+01 -3.739999999999998437e+01 -4.539999999999997016e+01 --1.014000000000000057e+02 --9.330000000000001137e+01 -3.360000000000000853e+01 -2.539999999999996660e+01 --1.126000000000000369e+02 -4.920000000000000995e+01 -3.750000000000000711e+01 -4.129999999999994031e+01 --3.149999999999996803e+01 -2.739999999999997016e+01 -3.729999999999996874e+01 -4.159999999999994458e+01 -3.479999999999996163e+01 -5.599999999999999289e+01 -4.060000000000000853e+01 -5.659999999999992326e+01 -5.379999999999994742e+01 -2.959999999999994102e+01 -1.559999999999993037e+01 -5.939999999999992752e+01 --9.590000000000000568e+01 -6.570000000000000284e+01 -3.979999999999994031e+01 -2.809999999999994813e+01 --1.135000000000000142e+02 --1.106000000000000085e+02 --2.570000000000003837e+01 -4.089999999999990621e+01 -3.190000000000000213e+01 -4.170000000000001705e+01 -1.840000000000000213e+01 -2.529999999999992966e+01 --1.035000000000000000e+02 --1.022000000000000028e+02 -5.520000000000000995e+01 -4.499999999999993605e+01 --7.560000000000007958e+01 --7.079999999999999716e+01 --1.030999999999999943e+02 --7.870000000000007390e+01 --9.440000000000000568e+01 -3.189999999999987423e+01 -5.620000000000002416e+01 --1.027999999999999972e+02 --1.000000000000000000e+02 -1.719999999999992824e+01 --6.029999999999960636e+01 -3.249999999999993605e+01 -7.199999999999939782e+00 -3.249999999999986500e+01 -1.189999999999997726e+01 -1.869999999999994955e+01 --4.489999999999964331e+01 --7.600000000000004974e+00 -2.429999999999999716e+01 --1.020999999999999943e+02 -1.949999999999995381e+01 -2.559999999999997300e+01 -6.999999999999979572e+00 -4.359999999999996589e+01 -4.039999999999991331e+01 -2.289999999999995950e+01 -2.919999999999994955e+01 -2.670000000000000639e+01 -3.060000000000007603e+01 -3.119999999999995666e+01 -4.209999999999993037e+01 -2.869999999999992824e+01 --4.039999999999994174e+01 -1.439999999999992752e+01 --2.200000000000003286e+00 -2.699999999999985079e+00 -1.589999999999991687e+01 --1.500000000000021538e+00 -1.969999999999989271e+01 --1.194000000000000199e+02 -1.959999999999993392e+01 -1.369999999999999396e+01 -3.209999999999990905e+01 -9.900000000000003908e+00 --7.199999999999998401e+00 --2.199999999999997513e+00 -2.499999999999992539e+01 -1.059999999999997478e+01 -3.450000000000001421e+01 -1.629999999999991545e+01 -2.269999999999986784e+01 -4.380000000000002558e+01 -3.440000000000000568e+01 -2.789999999999985292e+01 -2.539999999999993463e+01 -3.260000000000001563e+01 -1.900000000000010125e+00 -2.289999999999995595e+01 --6.729999999999962768e+01 -4.459999999999996589e+01 -4.199999999999982414e+00 -2.309999999999989484e+01 -2.899999999999994582e+00 -5.130000000000005400e+01 -4.049999999999992184e+01 -2.969999999999993889e+01 --1.062999999999999972e+02 -2.159999999999994458e+01 -1.649999999999997158e+01 --1.140000000000001279e+01 -1.759999999999995524e+01 --1.089000000000000057e+02 -7.699999999999997513e+00 -4.000000000000006883e-01 -1.689999999999990976e+01 --1.459999999999998863e+02 -1.670000000000000284e+01 -7.499999999999898748e+00 --5.599999999999998757e+00 -2.400000000000014566e+00 -4.659999999999991616e+01 --6.500000000000005329e+00 -2.759999999999993747e+01 --1.335000000000000568e+02 -4.970000000000002416e+01 -3.310000000000002274e+01 -1.329999999999994920e+01 -2.719999999999997087e+01 -2.809999999999985931e+01 --1.022999999999999972e+02 -1.539999999999997371e+01 -1.749999999999992184e+01 -3.630000000000001847e+01 --5.409999999999966036e+01 -2.959999999999994458e+01 -3.380000000000005400e+01 -1.509999999999998010e+01 --3.709999999999992326e+01 -1.179999999999998472e+01 --1.109000000000000057e+02 --1.306000000000000796e+02 --9.340000000000000568e+01 -2.149999999999995737e+01 -2.209999999999998366e+01 --1.000000000000013101e+00 --1.899999999999996803e+00 -1.239999999999993463e+01 -1.340000000000000036e+01 --1.075000000000000000e+02 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+3.000000000000000000e+02 +3.000000000000000000e+02 +3.000000000000000000e+02 diff --git a/README.md b/README.md new file mode 100644 index 0000000..d790b95 --- /dev/null +++ b/README.md @@ -0,0 +1,29 @@ +# QtableV1 + +## --welocomeToV2--- ## + +Copy&Run command + +Example: + +```terminal +python3 learning_node.py --n_actions_enable 3 --GOAL_POSITION .03 1.9777 .0 +``` + +## Args Options + +|args |type|description| +|-----|--|---------| +--log_file_dir| str | '/Data/Log_learning'(default) +--Q_source_dir| str | '/Data/Log_learning/Qtable.csv'(default) +--max_episodes| int | MAX_EPISODES = 10 (default) +--max_step_per_episodes| int| MAX_STEPS_PER_EPISODE = 500 (default) +--max_episodes_before_save| int|MAX_EPISODES_BEFORE_SAVE = 5 (default) +--exploration_func| int|1:Softmax(default), 2:Epsilon greedy +--get_best | bool| save Qtable_best when minimize rel_time --> True | False +--resume | bool| continue learning with same Qtable True | False +--n_actions_enable| int | number of action that enable(3-5 default:4) --> 0:forward, 1:CW, 2:CCW, 3:stop, 4:superForward +--radiaus_reduce_rate| float | RADIUS_REDUCE_RATE = .5 (default) +--reward_threshold| int | REWARD_THRESHOLD = -200 (default) +--GOAL_POSITION| float | GOAL_POSITION = (.03, 1.9777, .0) (default) +--GOAL_RADIUS| float | GOAL_RADIUS = .06 (default) diff --git a/scrpits/Control.py b/scrpits/Control.py index 1293d78..793befe 100644 --- a/scrpits/Control.py +++ b/scrpits/Control.py @@ -1,201 +1,234 @@ -#!/usr/bin/env python - -from time import time -from std_msgs.msg import String -from geometry_msgs.msg import Twist -from nav_msgs.msg import Odometry -from gazebo_msgs.msg import ModelState -from math import * -import numpy as np -from tf_transformations import euler_from_quaternion, quaternion_from_euler - -# Q-learning speed parameters -CONST_LINEAR_SPEED_FORWARD = 0.08 -CONST_ANGULAR_SPEED_FORWARD = 0.0 -CONST_LINEAR_SPEED_TURN = 0.06 -CONST_ANGULAR_SPEED_TURN = 0.4 - -# Feedback control parameters -K_RO = 2 -K_ALPHA = 15 -K_BETA = -3 -V_CONST = 0.1 # [m/s] - -# Goal reaching threshold -GOAL_DIST_THRESHOLD = 0.1 # [m] -GOAL_ANGLE_THRESHOLD = 15 # [degrees] - -# Get theta in [radians] -def getRotation(odomMsg): - orientation_q = odomMsg.pose.pose.orientation - orientation_list = [ orientation_q.x, orientation_q.y, orientation_q.z, orientation_q.w] - (roll, pitch, yaw) = euler_from_quaternion(orientation_list) - return yaw - -# Get (x,y) coordinates in [m] -def getPosition(odomMsg): - x = odomMsg.pose.pose.position.x - y = odomMsg.pose.pose.position.y - return ( x , y) - -# Get linear speed in [m/s] -def getLinVel(odomMsg): - return odomMsg.twist.twist.linear.x - -# Get angular speed in [rad/s] - z axis -def getAngVel(odomMsg): - return odomMsg.twist.twist.angular.z - -# Create rosmsg Twist() -def createVelMsg(v,w): - velMsg = Twist() - velMsg.linear.x = v - velMsg.linear.y = 0. - velMsg.linear.z = 0. - velMsg.angular.x = 0. - velMsg.angular.y = 0. - velMsg.angular.z = w - return velMsg - -# Go forward command -def robotGoForward(velPub): - velMsg = createVelMsg(CONST_LINEAR_SPEED_FORWARD,CONST_ANGULAR_SPEED_FORWARD) - velPub.publish(velMsg) - -# Turn left command -def robotTurnLeft(velPub): - velMsg = createVelMsg(CONST_LINEAR_SPEED_TURN,+CONST_ANGULAR_SPEED_TURN) - velPub.publish(velMsg) - -# Turn right command -def robotTurnRight(velPub): - velMsg = createVelMsg(CONST_LINEAR_SPEED_TURN,-CONST_ANGULAR_SPEED_TURN) - velPub.publish(velMsg) - -# Stop command -def robotStop(velPub): - velMsg = createVelMsg(0.0,0.0) - velPub.publish(velMsg) - -# Set robot position and orientation -def robotSetPos(setPosPub, x, y, theta): - checkpoint = ModelState() - - checkpoint.model_name = 'turtlebot3_burger' - - checkpoint.pose.position.x = float(x) - checkpoint.pose.position.y = float(y) - checkpoint.pose.position.z = 0.0 - - [x_q,y_q,z_q,w_q] = quaternion_from_euler(0.0,0.0,radians(theta)) - - checkpoint.pose.orientation.x = x_q - checkpoint.pose.orientation.y = y_q - checkpoint.pose.orientation.z = z_q - checkpoint.pose.orientation.w = w_q - - checkpoint.twist.linear.x = 0.0 - checkpoint.twist.linear.y = 0.0 - checkpoint.twist.linear.z = 0.0 - - checkpoint.twist.angular.x = 0.0 - checkpoint.twist.angular.y = 0.0 - checkpoint.twist.angular.z = 0.0 - - setPosPub.publish(checkpoint) - return ( x , y , theta ) - -# Set random initial robot position and orientation -def robotSetRandomPos(setPosPub): - x_range = np.array([-0.4, 0.6, 0.6, -1.4, -1.4, 2.0, 2.0, -2.5, 1.0, -1.0]) - y_range = np.array([-0.4, 0.6, -1.4, 0.6, -1.4, 1.0, -1.0, 0.0, 2.0, 2.0]) - theta_range = np.arange(0, 360, 15) - #theta_range = np.array([0, 30, 45, 60, 75, 90]) - - ind = np.random.randint(0,len(x_range)) - ind_theta = np.random.randint(0,len(theta_range)) - - x = x_range[ind] - y = y_range[ind] - theta = theta_range[ind_theta] - - checkpoint = ModelState() - - checkpoint.model_name = 'turtlebot3_burger' - - checkpoint.pose.position.x = x - checkpoint.pose.position.y = y - checkpoint.pose.position.z = 0.0 - - [x_q,y_q,z_q,w_q] = quaternion_from_euler(0.0,0.0,radians(theta)) - - checkpoint.pose.orientation.x = x_q - checkpoint.pose.orientation.y = y_q - checkpoint.pose.orientation.z = z_q - checkpoint.pose.orientation.w = w_q - - checkpoint.twist.linear.x = 0.0 - checkpoint.twist.linear.y = 0.0 - checkpoint.twist.linear.z = 0.0 - - checkpoint.twist.angular.x = 0.0 - checkpoint.twist.angular.y = 0.0 - checkpoint.twist.angular.z = 0.0 - - setPosPub.publish(checkpoint) - return ( x , y , theta ) - -# Perform an action -def robotDoAction(velPub, action): - status = 'robotDoAction => OK' - if action == 0: - robotGoForward(velPub) - elif action == 1: - robotTurnLeft(velPub) - elif action == 2: - robotTurnRight(velPub) - else: - status = 'robotDoAction => INVALID ACTION' - robotGoForward(velPub) - - return status - -# Feedback Control Algorithm -def robotFeedbackControl(velPub, x, y, theta, x_goal, y_goal, theta_goal): - # theta goal normalization - if theta_goal >= pi: - theta_goal_norm = theta_goal - 2 * pi - else: - theta_goal_norm = theta_goal - - ro = sqrt( pow( ( x_goal - x ) , 2 ) + pow( ( y_goal - y ) , 2) ) - lamda = atan2( y_goal - y , x_goal - x ) - - alpha = (lamda - theta + pi) % (2 * pi) - pi - beta = (theta_goal - lamda + pi) % (2 * pi) - pi - - if ro < GOAL_DIST_THRESHOLD and degrees(abs(theta-theta_goal_norm)) < GOAL_ANGLE_THRESHOLD: - status = 'Goal position reached!' - v = 0 - w = 0 - v_scal = 0 - w_scal = 0 - else: - status = 'Goal position not reached!' - v = K_RO * ro - w = K_ALPHA * alpha + K_BETA * beta - v_scal = v / abs(v) * V_CONST - w_scal = w / abs(v) * V_CONST - - velMsg = createVelMsg(v_scal, w_scal) - velPub.publish(velMsg) - - return status - -# Stability Condition -def check_stability(k_rho, k_alpha, k_beta): - return k_rho > 0 and k_beta < 0 and k_alpha > k_rho - -# Strong Stability Condition -def check_strong_stability(k_rho, k_alpha, k_beta): - return k_rho > 0 and k_beta < 0 and k_alpha + 5 * k_beta / 3 - 2 * k_rho / np.pi > 0 +#!/usr/bin/env python + +from time import time +from std_msgs.msg import String +from geometry_msgs.msg import Twist +from nav_msgs.msg import Odometry +from gazebo_msgs.msg import ModelState +from math import * +import numpy as np +from tf_transformations import euler_from_quaternion, quaternion_from_euler + +# Q-learning speed parameters +CONST_LINEAR_SPEED_FORWARD = 0.1 +CONST_ANGULAR_SPEED_FORWARD = 0.0 + +CONST_LINEAR_SPEED_TURN = 0.05 + +CONST_ANGULAR_SPEED_TURN = 0.5 +CONST_ANGULAR_SPEED_CW = 0.69 + +# Feedback control parameters +K_RO = 2 +K_ALPHA = 15 +K_BETA = -3 +V_CONST = 0.1 # [m/s] + +# Goal reaching threshold +GOAL_DIST_THRESHOLD = 0.05 # [m] +GOAL_ANGLE_THRESHOLD = 15 # [degrees] + +# Get theta in [radians] +def getRotation(odomMsg): + orientation_q = odomMsg.pose.pose.orientation + orientation_list = [ orientation_q.x, orientation_q.y, orientation_q.z, orientation_q.w] + (roll, pitch, yaw) = euler_from_quaternion(orientation_list) + return yaw + +# Get (x,y) coordinates in [m] +def getPosition(odomMsg): + x = odomMsg.pose.pose.position.x + y = odomMsg.pose.pose.position.y + return ( x , y) + +# Get linear speed in [m/s] +def getLinVel(odomMsg): + return odomMsg.twist.twist.linear.x + +# Get angular speed in [rad/s] - z axis +def getAngVel(odomMsg): + return odomMsg.twist.twist.angular.z + +# Create rosmsg Twist() +def createVelMsg(v,w): + velMsg = Twist() + velMsg.linear.x = float(v) + velMsg.linear.y = 0. + velMsg.linear.z = 0. + velMsg.angular.x = 0. + velMsg.angular.y = 0. + velMsg.angular.z = float(w) + return velMsg + +# Go forward command +def robotGoForward(velPub): + velMsg = createVelMsg(CONST_LINEAR_SPEED_FORWARD,CONST_ANGULAR_SPEED_FORWARD) + velPub.publish(velMsg) + +# Go 2 x forward command +def robotGoSuperForward(velPub): + velMsg = createVelMsg(2*CONST_LINEAR_SPEED_FORWARD,CONST_ANGULAR_SPEED_FORWARD) + velPub.publish(velMsg) + +# Go backward command +def robotGoBackward(velPub): + velMsg = createVelMsg(-CONST_LINEAR_SPEED_FORWARD,CONST_ANGULAR_SPEED_FORWARD) + velPub.publish(velMsg) + +# Turn left command +def robotTurnLeft(velPub): + velMsg = createVelMsg(CONST_LINEAR_SPEED_TURN,CONST_ANGULAR_SPEED_TURN) + velPub.publish(velMsg) + +# Turn right command +def robotTurnRight(velPub): + velMsg = createVelMsg(CONST_LINEAR_SPEED_TURN,-CONST_ANGULAR_SPEED_TURN) + velPub.publish(velMsg) + +# Stop command +def robotStop(velPub): + velMsg = createVelMsg(0.0,0.0) + velPub.publish(velMsg) +# CW command +def robotCW(velPub): + velMsg = createVelMsg(0.0, -CONST_ANGULAR_SPEED_TURN) + velPub.publish(velMsg) + +# CCW command +def robotCCW(velPub): + velMsg = createVelMsg(0.0, CONST_ANGULAR_SPEED_TURN) + velPub.publish(velMsg) +# Set robot position and orientation +def robotSetPos(setPosPub, x, y, theta): + checkpoint = ModelState() + + checkpoint.model_name = 'turtlebot3_burger' + + checkpoint.pose.position.x = float(x) + checkpoint.pose.position.y = float(y) + checkpoint.pose.position.z = 0.0 + + [x_q,y_q,z_q,w_q] = quaternion_from_euler(0.0,0.0,radians(theta)) + + checkpoint.pose.orientation.x = x_q + checkpoint.pose.orientation.y = y_q + checkpoint.pose.orientation.z = z_q + checkpoint.pose.orientation.w = w_q + + checkpoint.twist.linear.x = 0.0 + checkpoint.twist.linear.y = 0.0 + checkpoint.twist.linear.z = 0.0 + + checkpoint.twist.angular.x = 0.0 + checkpoint.twist.angular.y = 0.0 + checkpoint.twist.angular.z = 0.0 + + setPosPub.publish(checkpoint) + return ( x , y , theta ) + +# Set random initial robot position and orientation +def robotSetRandomPos(setPosPub): + x_range = np.array([-0.4, 0.6, 0.6, -1.4, -1.4, 2.0, 2.0, -2.5, 1.0, -1.0]) + y_range = np.array([-0.4, 0.6, -1.4, 0.6, -1.4, 1.0, -1.0, 0.0, 2.0, 2.0]) + theta_range = np.arange(0, 360, 15) + #theta_range = np.array([0, 30, 45, 60, 75, 90]) + + ind = np.random.randint(0,len(x_range)) + ind_theta = np.random.randint(0,len(theta_range)) + + x = x_range[ind] + y = y_range[ind] + theta = theta_range[ind_theta] + + checkpoint = ModelState() + + checkpoint.model_name = 'turtlebot3_burger' + + checkpoint.pose.position.x = x + checkpoint.pose.position.y = y + checkpoint.pose.position.z = 0.0 + + [x_q,y_q,z_q,w_q] = quaternion_from_euler(0.0,0.0,radians(theta)) + + checkpoint.pose.orientation.x = x_q + checkpoint.pose.orientation.y = y_q + checkpoint.pose.orientation.z = z_q + checkpoint.pose.orientation.w = w_q + + checkpoint.twist.linear.x = 0.0 + checkpoint.twist.linear.y = 0.0 + checkpoint.twist.linear.z = 0.0 + + checkpoint.twist.angular.x = 0.0 + checkpoint.twist.angular.y = 0.0 + checkpoint.twist.angular.z = 0.0 + + setPosPub.publish(checkpoint) + return ( x , y , theta ) + +# Perform an action +def robotDoAction(velPub, action): + status = 'robotDoAction => OK' + if action == 0: + robotGoForward(velPub) + # elif action == 1: + # robotTurnLeft(velPub) + # elif action == 2: + # robotTurnRight(velPub) +######################################## + elif action == 4: + robotGoSuperForward(velPub) + # elif action == 4: + # robotGoBackward(velPub) + elif action == 3: + robotStop(velPub) + elif action == 1: + robotCW(velPub) + elif action == 2: + robotCCW(velPub) + else: + status = 'robotDoAction => INVALID ACTION' + robotGoForward(velPub) + + return status + +# Feedback Control Algorithm +def robotFeedbackControl(velPub, x, y, theta, x_goal, y_goal, theta_goal): + # theta goal normalization + if theta_goal >= np.pi: + theta_goal_norm = theta_goal - 2 * np.pi + else: + theta_goal_norm = theta_goal + + ro = sqrt( pow( ( x_goal - x ) , 2 ) + pow( ( y_goal - y ) , 2) ) + lamda = atan2( y_goal - y , x_goal - x ) + + alpha = (lamda - theta + np.pi) % (2 * np.pi) - np.pi + beta = (theta_goal - lamda + np.pi) % (2 * np.pi) - np.pi + + if ro < GOAL_DIST_THRESHOLD: # and degrees(abs(theta-theta_goal_norm)) < GOAL_ANGLE_THRESHOLD: + status = 'Goal position reached!' + print('GOAL REACH') + v = 0 + w = 0 + v_scal = 0 + w_scal = 0 + else: + status = 'Goal position not reached!' + v = K_RO * ro + w = K_ALPHA * alpha + K_BETA * beta + v_scal = v / abs(v) * V_CONST + w_scal = w / abs(v) * V_CONST + + velMsg = createVelMsg(v_scal, w_scal) + velPub.publish(velMsg) + + return status + +# Stability Condition +def check_stability(k_rho, k_alpha, k_beta): + return k_rho > 0 and k_beta < 0 and k_alpha > k_rho + +# Strong Stability Condition +def check_strong_stability(k_rho, k_alpha, k_beta): + return k_rho > 0 and k_beta < 0 and k_alpha + 5 * k_beta / 3 - 2 * k_rho / np.pi > 0 diff --git a/scrpits/Lidar.py b/scrpits/Lidar.py index aa4a177..29a0f01 100644 --- a/scrpits/Lidar.py +++ b/scrpits/Lidar.py @@ -1,140 +1,212 @@ -#! /usr/bin/env python - -import numpy as np -from math import * -from std_msgs.msg import String -from sensor_msgs.msg import LaserScan - -MAX_LIDAR_DISTANCE = 1.0 -COLLISION_DISTANCE = 0.14 # LaserScan.range_min = 0.1199999 -NEARBY_DISTANCE = 0.45 - -ZONE_0_LENGTH = 0.4 -ZONE_1_LENGTH = 0.7 - -ANGLE_MAX = 360 - 1 -ANGLE_MIN = 1 - 1 -HORIZON_WIDTH = 75 - -# Convert LasecScan msg to array -def lidarScan(msgScan): - distances = np.array([]) - angles = np.array([]) - - for i in range(len(msgScan.ranges)): - angle = degrees(i * msgScan.angle_increment) - if ( msgScan.ranges[i] > MAX_LIDAR_DISTANCE ): - distance = MAX_LIDAR_DISTANCE - elif ( msgScan.ranges[i] < msgScan.range_min ): - distance = msgScan.range_min - # For real robot - protection - if msgScan.ranges[i] < 0.01: - distance = MAX_LIDAR_DISTANCE - else: - distance = msgScan.ranges[i] - - distances = np.append(distances, distance) - angles = np.append(angles, angle) - - # distances in [m], angles in [degrees] - return ( distances, angles ) - -# Discretization of lidar scan -def scanDiscretization(state_space, lidar): - x1 = 2 # Left zone (no obstacle detected) - x2 = 2 # Right zone (no obstacle detected) - x3 = 3 # Left sector (no obstacle detected) - x4 = 3 # Right sector (no obstacle detected) - - # Find the left side lidar values of the vehicle - lidar_left = min(lidar[(ANGLE_MIN):(ANGLE_MIN + HORIZON_WIDTH)]) - if ZONE_1_LENGTH > lidar_left > ZONE_0_LENGTH: - x1 = 1 # zone 1 - elif lidar_left <= ZONE_0_LENGTH: - x1 = 0 # zone 0 - - # Find the right side lidar values of the vehicle - lidar_right = min(lidar[(ANGLE_MAX - HORIZON_WIDTH):(ANGLE_MAX)]) - if ZONE_1_LENGTH > lidar_right > ZONE_0_LENGTH: - x2 = 1 # zone 1 - elif lidar_right <= ZONE_0_LENGTH: - x2 = 0 # zone 0 - - # Detection of object in front of the robot - if ( min(lidar[(ANGLE_MAX - HORIZON_WIDTH // 3):(ANGLE_MAX)]) < 1.0 ) or ( min(lidar[(ANGLE_MIN):(ANGLE_MIN + HORIZON_WIDTH // 3)]) < 1.0 ): - object_front = True - else: - object_front = False - - # Detection of object on the left side of the robot - if min(lidar[(ANGLE_MIN):(ANGLE_MIN + 2 * HORIZON_WIDTH // 3)]) < 1.0: - object_left = True - else: - object_left = False - - # Detection of object on the right side of the robot - if min(lidar[(ANGLE_MAX - 2 * HORIZON_WIDTH // 3):(ANGLE_MAX)]) < 1.0: - object_right = True - else: - object_right = False - - # Detection of object on the far left side of the robot - if min(lidar[(ANGLE_MIN + HORIZON_WIDTH // 3):(ANGLE_MIN + HORIZON_WIDTH)]) < 1.0: - object_far_left = True - else: - object_far_left = False - - # Detection of object on the far right side of the robot - if min(lidar[(ANGLE_MAX - HORIZON_WIDTH):(ANGLE_MAX - HORIZON_WIDTH // 3)]) < 1.0: - object_far_right = True - else: - object_far_right = False - - # The left sector of the vehicle - if ( object_front and object_left ) and ( not object_far_left ): - x3 = 0 # sector 0 - elif ( object_left and object_far_left ) and ( not object_front ): - x3 = 1 # sector 1 - elif object_front and object_left and object_far_left: - x3 = 2 # sector 2 - - if ( object_front and object_right ) and ( not object_far_right ): - x4 = 0 # sector 0 - elif ( object_right and object_far_right ) and ( not object_front ): - x4 = 1 # sector 1 - elif object_front and object_right and object_far_right: - x4 = 2 # sector 2 - - # Find the state space index of (x1,x2,x3,x4) in Q table - ss = np.where(np.all(state_space == np.array([x1,x2,x3,x4]), axis = 1)) - state_ind = int(ss[0]) - - return ( state_ind, x1, x2, x3 , x4 ) - -# Check - crash -def checkCrash(lidar): - lidar_horizon = np.concatenate((lidar[(ANGLE_MIN + HORIZON_WIDTH):(ANGLE_MIN):-1],lidar[(ANGLE_MAX):(ANGLE_MAX - HORIZON_WIDTH):-1])) - W = np.linspace(1.2, 1, len(lidar_horizon) // 2) - W = np.append(W, np.linspace(1, 1.2, len(lidar_horizon) // 2)) - if np.min( W * lidar_horizon ) < COLLISION_DISTANCE: - return True - else: - return False - -# Check - object nearby -def checkObjectNearby(lidar): - lidar_horizon = np.concatenate((lidar[(ANGLE_MIN + HORIZON_WIDTH):(ANGLE_MIN):-1],lidar[(ANGLE_MAX):(ANGLE_MAX - HORIZON_WIDTH):-1])) - W = np.linspace(1.4, 1, len(lidar_horizon) // 2) - W = np.append(W, np.linspace(1, 1.4, len(lidar_horizon) // 2)) - if np.min( W * lidar_horizon ) < NEARBY_DISTANCE: - return True - else: - return False - -# Check - goal near -def checkGoalNear(x, y, x_goal, y_goal): - ro = sqrt( pow( ( x_goal - x ) , 2 ) + pow( ( y_goal - y ) , 2) ) - if ro < 0.3: - return True - else: - return False +#! /usr/bin/env python + +import numpy as np +from math import * +from std_msgs.msg import String +from sensor_msgs.msg import LaserScan + +MAX_LIDAR_DISTANCE = .8 +COLLISION_DISTANCE = 0.08 # LaserScan.range_min = 0.1199999 +NEARBY_DISTANCE = 0.45 + +ZONE_0_LENGTH = .25 +ZONE_1_LENGTH = .5 + +ANGLE_MAX = 360 #360 degree +ANGLE_MIN = 1 - 1 #0 degree +ANGLE_BACK = 180 #180 degree +# HORIZON_WIDTH = 75 #original +HORIZON_WIDTH = [9, 16, 65, 9] #9:x1, x2, x7 16:x3, x4 25:x5, x6 + +# Convert LasecScan msg to array +def lidarScan(msgScan): + distances = np.array([]) + angles = np.array([]) + + for i in range(len(msgScan.ranges)): + angle = degrees(i * msgScan.angle_increment) + if ( msgScan.ranges[i] > MAX_LIDAR_DISTANCE ): + distance = MAX_LIDAR_DISTANCE + elif ( msgScan.ranges[i] < msgScan.range_min ): + distance = msgScan.range_min + # For real robot - protection + if msgScan.ranges[i] < 0.01: + distance = MAX_LIDAR_DISTANCE + else: + distance = msgScan.ranges[i] + + distances = np.append(distances, distance) + angles = np.append(angles, angle) + + # distances in [m], angles in [degrees] + return ( distances, angles ) + +# Discretization of lidar scan +def scanDiscretization(state_space, lidar, target_pos, robot_pose, robot_prev_pose, max_dist, goal_radius): + ### now --> 2304*3*4 stage + x1 = 1 # no obstacle + x2 = 0 + x3 = 2 + x4 = 3 + x5 = 3 + x6 = 2 + x7 = 2 + x8 = 1 + + x9 = 2 + x10 = 1 + + length_lidar = len(lidar) + # print(f'length_lidar: {length_lidar}') + ratio = length_lidar / 360 + + ############################################################################### + ##HORIZON_WIDTH[0] --> 9 degree :x1, x8 + # lidar_x1 = min(lidar[81: 90]) + lidar_x1 = min(lidar[round(ratio*(ANGLE_MIN + HORIZON_WIDTH[1] + HORIZON_WIDTH[2])): round(ratio*(ANGLE_MIN + HORIZON_WIDTH[1] + HORIZON_WIDTH[2] + HORIZON_WIDTH[3])) ]) + if ZONE_0_LENGTH <= lidar_x1 <= ZONE_1_LENGTH: + x1 = 1 + else: + x1 = 0 + + # lidar_x8 = min(lidar[270: 279]) + lidar_x8 = min(lidar[round(ratio*(ANGLE_MAX - HORIZON_WIDTH[1] - HORIZON_WIDTH[2] - HORIZON_WIDTH[3])):round(ratio*(ANGLE_MAX - HORIZON_WIDTH[1] - HORIZON_WIDTH[2])) ]) + if ZONE_0_LENGTH <= lidar_x8 <= ZONE_1_LENGTH: + x1 = 1 + else: + x1 = 0 + + ############################################################################### + ##HORIZON_WIDTH[2] --> 65 degree(25 to 90) :x2, x7 + # lidar_x2 = min(lidar[25: 90]) + lidar_x2 = min(lidar[round(ratio*(ANGLE_MIN + HORIZON_WIDTH[0] + HORIZON_WIDTH[1])): round(ratio*(ANGLE_MIN + HORIZON_WIDTH[0] + HORIZON_WIDTH[1] + HORIZON_WIDTH[2] ))]) + if ZONE_0_LENGTH <= lidar_x2: + x2 = 0 + else: + x2 = 1 + + # lidar_x7 = min(lidar[270: 335]) + lidar_x7 = min(lidar[round(ratio*(ANGLE_MAX - HORIZON_WIDTH[0] - HORIZON_WIDTH[1] - HORIZON_WIDTH[2] )):round(ratio*(ANGLE_MAX - HORIZON_WIDTH[0] - HORIZON_WIDTH[1])) ]) + if ZONE_0_LENGTH <= lidar_x7: + x7 = 0 + else: + x7 = 1 + + ############################################################################### + ##HORIZON_WIDTH[1] --> 16 degree (9 to 25):x3, x6 + # lidar_x3 = min(lidar[9: 25]) + lidar_x3 = min( lidar[round(ratio*(ANGLE_MIN + HORIZON_WIDTH[0])): round(ratio*(ANGLE_MIN + HORIZON_WIDTH[0] + HORIZON_WIDTH[1]))]) + if ZONE_1_LENGTH < lidar_x3: + x3 = 2 + elif ZONE_0_LENGTH < lidar_x3 < ZONE_1_LENGTH: + x3 = 1 + elif lidar_x3 < ZONE_0_LENGTH: + x3 = 0 + + # lidar_x6 = min(lidar[335: 351]) + lidar_x6 = min(lidar[round(ratio*(ANGLE_MAX - HORIZON_WIDTH[0] - HORIZON_WIDTH[1])):round(ratio*(ANGLE_MAX - HORIZON_WIDTH[0]))]) + if ZONE_1_LENGTH < lidar_x6: + x6 = 2 + elif ZONE_0_LENGTH < lidar_x6 < ZONE_1_LENGTH: + x6 = 1 + elif lidar_x6 < ZONE_0_LENGTH: + x6 = 0 + + ############################################################################### + ##HORIZON_WIDTH[0] --> 9 degree :x4, x5 + # lidar_x4 = min(lidar[0: 10]) + lidar_x4 = min(lidar[ANGLE_MIN: round(ratio*(ANGLE_MIN + HORIZON_WIDTH[0]))]) + if MAX_LIDAR_DISTANCE < lidar_x4: + x4 = 3 + elif ZONE_1_LENGTH < lidar_x4: + x4 = 2 + elif ZONE_0_LENGTH < lidar_x4 < ZONE_1_LENGTH: + x4 = 1 + elif lidar_x4 < ZONE_0_LENGTH: + x4 = 0 + + # from index 351 to 0 + # lidar_x5 = min(lidar[350: 360] + lidar[0]) + lidar_x5 = min(lidar[round(ratio*(ANGLE_MAX - HORIZON_WIDTH[0] )):round(ratio*(ANGLE_MAX))] + lidar[0]) + + if MAX_LIDAR_DISTANCE < lidar_x5: + x5 = 3 + elif ZONE_1_LENGTH < lidar_x5: + x5 = 2 + elif ZONE_0_LENGTH < lidar_x5 < ZONE_1_LENGTH: + x5 = 1 + elif lidar_x5 < ZONE_0_LENGTH: + x5 = 0 + + ############################################################################### + # distance + target_pos = np.array(target_pos) + robot_pose = np.array(robot_pose) + + dist = np.linalg.norm(target_pos - robot_pose) + + if dist > .5 * max_dist: + x9 = 2 + elif dist > 2*goal_radius: + x9 = 1 + else: + x9 = 0 + + robot_pose = np.array([robot_pose[0], robot_pose[1]]) + robot_prev_pose = np.array([robot_prev_pose[0], robot_prev_pose[1]]) + target_pos = np.array([target_pos[0], target_pos[1]]) + + # vector from robot to target + d_vec = target_pos - robot_pose + # vexor from robot to robot_prev + v_vec = robot_pose - robot_prev_pose + + d_vec3d = np.array([d_vec[0], d_vec[1], 0]) + v_vec3d = np.array([v_vec[0], v_vec[1], 0]) + + if np.dot(d_vec, v_vec) < 0: + x10 = 0 # going back + else: + if np.arccos(np.dot(d_vec, v_vec) / (np.linalg.norm(d_vec) * np.linalg.norm(v_vec))) < np.arcsin(goal_radius)/dist: + x10 = 1 # going to target + elif np.cross(d_vec3d, v_vec3d)[2] < 0: + x10 = 2 # too much right + else: + x10 = 3 # too much left + + + + ss = np.where(np.all(state_space == np.array([x1, x2, x3, x4, x5, x6, x7, x8, x9, x10]), axis = 1)) + state_ind = int(ss[0]) + + return ( state_ind, x1, x2, x3 , x4 , x5, x6, x7, x8, x9, x10) + +# Check - crash +def checkCrash(lidar): + # lidar_horizon = np.concatenate((lidar[(ANGLE_MIN + sum(HORIZON_WIDTH)):(ANGLE_MIN):-1],lidar[(ANGLE_MAX):(ANGLE_MAX - sum(HORIZON_WIDTH)):-1])) + # W = np.linspace(1.56, 1, len(lidar_horizon) // 2) + # W = np.append(W, np.linspace(1, 1.56, len(lidar_horizon) // 2)) + # if np.min( W * lidar_horizon ) < COLLISION_DISTANCE: + if np.min(lidar) < COLLISION_DISTANCE: + return True + else: + return False + +# Check - object nearby +def checkObjectNearby(lidar): + lidar_horizon = np.concatenate((lidar[(ANGLE_MIN + sum(HORIZON_WIDTH)):(ANGLE_MIN):-1],lidar[(ANGLE_MAX):(ANGLE_MAX - sum(HORIZON_WIDTH)):-1])) + W = np.linspace(1.56, 1, len(lidar_horizon) // 2) + W = np.append(W, np.linspace(1, 1.56, len(lidar_horizon) // 2)) + if np.min( W * lidar_horizon ) < NEARBY_DISTANCE: + if np.min(lidar) < NEARBY_DISTANCE: + return True + else: + return False + +# Check - goal near +def checkGoalNear(x, y, x_goal, y_goal): + ro = sqrt( pow( ( x_goal - x ) , 2 ) + pow( ( y_goal - y ) , 2) ) + if ro < 0.3: + return True + else: + return False diff --git a/scrpits/Qlearning.py b/scrpits/Qlearning.py index e3f6aab..ff2d2aa 100644 --- a/scrpits/Qlearning.py +++ b/scrpits/Qlearning.py @@ -1,155 +1,254 @@ -#! /usr/bin/env python - -import numpy as np -from math import * -from std_msgs.msg import String -from itertools import product -from sensor_msgs.msg import LaserScan - -STATE_SPACE_IND_MAX = 144 - 1 -STATE_SPACE_IND_MIN = 1 - 1 -ACTIONS_IND_MAX = 2 -ACTIONS_IND_MIN = 0 - -ANGLE_MAX = 360 - 1 -ANGLE_MIN = 1 - 1 -HORIZON_WIDTH = 75 - -T_MIN = 0.001 - -# Create actions -def createActions(): - actions = np.array([0,1,2]) - return actions - -# Create state space for Q table -def createStateSpace(): - x1 = set((0,1,2)) - x2 = set((0,1,2)) - x3 = set((0,1,2,3)) - x4 = set((0,1,2,3)) - state_space = set(product(x1,x2,x3,x4)) - return np.array(list(state_space)) - -# Create Q table, dim: n_states x n_actions -def createQTable(n_states, n_actions): - #Q_table = np.random.uniform(low = -0.05, high = 0, size = (n_states,n_actions) ) - Q_table = np.zeros((n_states, n_actions)) - return Q_table - -# Read Q table from path -def readQTable(path): - Q_table = np.genfromtxt(path, delimiter = ' , ') - return Q_table - -# Write Q table to path -def saveQTable(path, Q_table): - np.savetxt(path, Q_table, delimiter = ' , ') - -# Select the best action a in state -def getBestAction(Q_table, state_ind, actions): - if STATE_SPACE_IND_MIN <= state_ind <= STATE_SPACE_IND_MAX: - status = 'getBestAction => OK' - a_ind = np.argmax(Q_table[state_ind,:]) - a = actions[a_ind] - else: - status = 'getBestAction => INVALID STATE INDEX' - a = getRandomAction(actions) - - return ( a, status ) - -# Select random action from actions -def getRandomAction(actions): - n_actions = len(actions) - a_ind = np.random.randint(n_actions) - return actions[a_ind] - -# Epsilog Greedy Exploration action chose -def epsiloGreedyExploration(Q_table, state_ind, actions, epsilon): - if np.random.uniform() > epsilon and STATE_SPACE_IND_MIN <= state_ind <= STATE_SPACE_IND_MAX: - status = 'epsiloGreedyExploration => OK' - ( a, status_gba ) = getBestAction(Q_table, state_ind, actions) - if status_gba == 'getBestAction => INVALID STATE INDEX': - status = 'epsiloGreedyExploration => INVALID STATE INDEX' - else: - status = 'epsiloGreedyExploration => OK' - a = getRandomAction(actions) - - return ( a, status ) - -# SoftMax Selection -def softMaxSelection(Q_table, state_ind, actions, T): - if STATE_SPACE_IND_MIN <= state_ind <= STATE_SPACE_IND_MAX: - status = 'softMaxSelection => OK' - n_actions = len(actions) - P = np.zeros(n_actions) - - # Boltzman distribution - P = np.exp(Q_table[state_ind,:] / T) / np.sum(np.exp(Q_table[state_ind,:] / T)) - - if T < T_MIN or np.any(np.isnan(P)): - ( a, status_gba ) = getBestAction(Q_table, state_ind, actions) - if status_gba == 'getBestAction => INVALID STATE INDEX': - status = 'softMaxSelection => INVALID STATE INDEX' - else: - rnd = np.random.uniform() - status = 'softMaxSelection => OK' - if P[0] > rnd: - a = 0 - elif P[0] <= rnd and (P[0] + P[1]) > rnd: - a = 1 - elif (P[0] + P[1]) <= rnd: - a = 2 - else: - status = 'softMaxSelection => Boltzman distribution error => getBestAction ' - status = status + '\r\nP = (%f , %f , %f) , rnd = %f' % (P[0],P[1],P[2],rnd) - status = status + '\r\nQ(%d,:) = ( %f, %f, %f) ' % (state_ind,Q_table[state_ind,0],Q_table[state_ind,1],Q_table[state_ind,2]) - ( a, status_gba ) = getBestAction(Q_table, state_ind, actions) - if status_gba == 'getBestAction => INVALID STATE INDEX': - status = 'softMaxSelection => INVALID STATE INDEX' - else: - status = 'softMaxSelection => INVALID STATE INDEX' - a = getRandomAction(actions) - - return ( a, status ) - -# Reward function for Q-learning - table -def getReward(action, prev_action, lidar, prev_lidar, crash): - if crash: - terminal_state = True - reward = -100 - else: - lidar_horizon = np.concatenate((lidar[(ANGLE_MIN + HORIZON_WIDTH):(ANGLE_MIN):-1],lidar[(ANGLE_MAX):(ANGLE_MAX - HORIZON_WIDTH):-1])) - prev_lidar_horizon = np.concatenate((prev_lidar[(ANGLE_MIN + HORIZON_WIDTH):(ANGLE_MIN):-1],prev_lidar[(ANGLE_MAX):(ANGLE_MAX - HORIZON_WIDTH):-1])) - terminal_state = False - # Reward from action taken = fowrad -> +0.2 , turn -> -0.1 - if action == 0: - r_action = +0.2 - else: - r_action = -0.1 - # Reward from crash distance to obstacle change - W = np.linspace(0.9, 1.1, len(lidar_horizon) // 2) - W = np.append(W, np.linspace(1.1, 0.9, len(lidar_horizon) // 2)) - if np.sum( W * ( lidar_horizon - prev_lidar_horizon) ) >= 0: - r_obstacle = +0.2 - else: - r_obstacle = -0.2 - # Reward from turn left/right change - if ( prev_action == 1 and action == 2 ) or ( prev_action == 2 and action == 1 ): - r_change = -0.8 - else: - r_change = 0.0 - - # Cumulative reward - reward = r_action + r_obstacle + r_change - - return ( reward, terminal_state ) - -# Update Q-table values -def updateQTable(Q_table, state_ind, action, reward, next_state_ind, alpha, gamma): - if STATE_SPACE_IND_MIN <= state_ind <= STATE_SPACE_IND_MAX and STATE_SPACE_IND_MIN <= next_state_ind <= STATE_SPACE_IND_MAX: - status = 'updateQTable => OK' - Q_table[state_ind,action] = ( 1 - alpha ) * Q_table[state_ind,action] + alpha * ( reward + gamma * max(Q_table[next_state_ind,:]) ) - else: - status = 'updateQTable => INVALID STATE INDEX' - return ( Q_table, status ) +#! /usr/bin/env python + +import numpy as np +from math import * +from std_msgs.msg import String +from itertools import product +from sensor_msgs.msg import LaserScan +import time + +STATE_SPACE_IND_MAX = 27648 - 1 +STATE_SPACE_IND_MIN = 1 - 1 +ACTIONS_IND_MAX = 7 +ACTIONS_IND_MIN = 0 + +ANGLE_MAX = 360 - 1 +ANGLE_MIN = 1 - 1 +# HORIZON_WIDTH = 75 original +HORIZON_WIDTH = [9, 16, 56, 9] + +T_MIN = 0.001 + +# Create actions +def createActions(n_actions_enable): + # actions = np.array([0,1,2,3,4,5,6,7]) + actions = np.arange(n_actions_enable) + return actions +# forward, left, right, superForward, backward, stop, CW, CCW + +# Create state space for Q table +def createStateSpace(): + x1 = set((0,1)) + x2 = set((0,1)) + x3 = set((0,1,2)) + x4 = set((0,1,2,3)) + x5 = set((0,1,2,3)) + x6 = set((0,1,2)) + x7 = set((0,1)) + x8 = set((0,1)) + x9 = set((0,1,2)) + x10 = set((0,1,2,3)) + state_space = set(product(x1,x2,x3,x4,x5,x6,x7,x8,x9,x10)) + return np.array(list(state_space)) + +# Create Q table, dim: n_states x n_actions +def createQTable(n_states, n_actions): + # Q_table = np.random.uniform(low = -1, high = 1, size = (n_states,n_actions) ) + Q_table = np.zeros((n_states, n_actions)) + return Q_table + +# Read Q table from path +def readQTable(path): + Q_table = np.genfromtxt(path, delimiter = ' , ') + return Q_table + +# Write Q table to path +def saveQTable(path, Q_table): + np.savetxt(path, Q_table, delimiter = ' , ') + +# Select the best action a in state +def getBestAction(Q_table, state_ind, actions): + if STATE_SPACE_IND_MIN <= state_ind <= STATE_SPACE_IND_MAX: + status = 'getBestAction => OK' + a_ind = np.argmax(Q_table[state_ind,:]) + a = actions[a_ind] + else: + status = 'getBestAction => INVALID STATE INDEX' + a = getRandomAction(actions) + + return ( a, status ) + +# Select random action from actions +def getRandomAction(actions): + n_actions = len(actions) + a_ind = np.random.randint(n_actions) + return actions[a_ind] + +# Epsilog Greedy Exploration action chose +def epsiloGreedyExploration(Q_table, state_ind, actions, epsilon): + if np.random.uniform() > epsilon and STATE_SPACE_IND_MIN <= state_ind <= STATE_SPACE_IND_MAX: + status = 'epsiloGreedyExploration => OK' + ( a, status_gba ) = getBestAction(Q_table, state_ind, actions) + if status_gba == 'getBestAction => INVALID STATE INDEX': + status = 'epsiloGreedyExploration => INVALID STATE INDEX' + else: + status = 'epsiloGreedyExploration => OK' + a = getRandomAction(actions) + + return ( a, status ) + +# SoftMax Selection +def softMaxSelection(Q_table, state_ind, actions, T): + if STATE_SPACE_IND_MIN <= state_ind <= STATE_SPACE_IND_MAX: + status = 'softMaxSelection => OK' + n_actions = len(actions) + P_ac = np.zeros(n_actions) + + # Boltzman distribution + P_ac = np.exp(Q_table[state_ind,:] / T) / np.sum(np.exp(Q_table[state_ind,:] / T)) + + if T < T_MIN or np.any(np.isnan(P_ac)): + ( a, status_gba ) = getBestAction(Q_table, state_ind, actions) + if status_gba == 'getBestAction => INVALID STATE INDEX': + status = 'softMaxSelection => INVALID STATE INDEX' + else: + # rnd = np.random.uniform() + status = 'softMaxSelection => OK' + try: + a = np.random.choice(n_actions, 1, p = P_ac) + ################################### + except: + status = 'softMaxSelection => Boltzman distribution error => getBestAction ' + status = status + '\r\nP = (%f , %f , %f, %f, %f, %f, %f, %f) ' % (P_ac[0],P_ac[1],P_ac[2],P_ac[3],P_ac[4],P_ac[5], P_ac[6], P_ac[7]) + status = status + '\r\nQ(%d,:) = ( %f , %f , %f, %f, %f, %f, %f, %f) ' % (state_ind, Q_table[state_ind,0], Q_table[state_ind,1], Q_table[state_ind,2], Q_table[state_ind,3], Q_table[state_ind,4], Q_table[state_ind,5], Q_table[state_ind,6], Q_table[state_ind,7]) + ( a, status_gba ) = getBestAction(Q_table, state_ind, actions) + if status_gba == 'getBestAction => INVALID STATE INDEX': + status = 'softMaxSelection => INVALID STATE INDEX' + else: + status = 'softMaxSelection => INVALID STATE INDEX' + a = getRandomAction(actions) + + return ( a, status ) + +# Reward function for Q-learning - table +def getReward( action, + prev_action, + lidar, + prev_lidar, + crash, + current_position, + goal_position, + max_radius, + radius_reduce_rate, + nano_start_time, + nano_current_time, + goal_radius, + angle_state, + win_count): + + terminal_state = False + # init reward + reward = 0 + + # to do in learning_node file + # add time start for each episode + # add position start for each episode + # add current position for each step + # add goal position for each episode + # add max radius for each episode + # add radius reduce rate for each episode + + # time penalty + dist = np.linalg.norm(np.array(current_position) - np.array(goal_position)) + # nano time diff + time_diff = (nano_current_time - nano_start_time) + radius = max_radius - radius_reduce_rate * (time_diff) + if radius/max_radius < 0.1: + radius = max_radius * 0.1 + if dist < radius: + reward += .1 + else: + reward += - .69 + + # Crash panelty + if crash: + reward += -500 + + # facing wall panelty/rewards + lidar_horizon = np.concatenate((lidar[(ANGLE_MIN + sum(HORIZON_WIDTH[:2])):(ANGLE_MIN):-1],lidar[(ANGLE_MAX):(ANGLE_MAX - sum(HORIZON_WIDTH[:2])):-1])) + prev_lidar_horizon = np.concatenate((prev_lidar[(ANGLE_MIN + sum(HORIZON_WIDTH[:2])):(ANGLE_MIN):-1],prev_lidar[(ANGLE_MAX):(ANGLE_MAX - sum(HORIZON_WIDTH[:2])):-1])) + W = np.linspace(1, 1.1, len(lidar_horizon) // 2) + W = np.append(W, np.linspace(1.1, 1, len(lidar_horizon) // 2)) + if np.sum( W * ( lidar_horizon - prev_lidar_horizon) ) >= 0: + reward += +1 + else: + reward += -1 + + + #too close to wall + lidar_horizon = np.concatenate((lidar[(ANGLE_MIN + sum(HORIZON_WIDTH[:1])):(ANGLE_MIN):-1],lidar[(ANGLE_MAX):(ANGLE_MAX - sum(HORIZON_WIDTH[:1])):-1])) + if min(lidar_horizon) < 0.15: + reward += -5 + if min(lidar_horizon) < 0.1: + reward += -10 + + # action and prev_action is same and action is left or right + if (prev_action == 3 and action == 4) or (prev_action == 4 and action == 3): + reward += -1 + + #repeat stop penelty + if prev_action == 2 and action == 2: + reward += -1 + + #reach goal + if dist +0.2 , turn -> -0.1 + # if action == 0: + # r_action = +0.2 + # else: + # r_action = -0.1 + # # Reward from crash distance to obstacle change + # W = np.linspace(0.9, 1.1, len(lidar_horizon) // 2) + # W = np.append(W, np.linspace(1.1, 0.9, len(lidar_horizon) // 2)) + # if np.sum( W * ( lidar_horizon - prev_lidar_horizon) ) >= 0: + # r_obstacle = +0.2 + # else: + # r_obstacle = -0.2 + # # Reward from turn left/right change + # if ( prev_action == 1 and action == 2 ) or ( prev_action == 2 and action == 1 ): + # r_change = -0.8 + # else: + # r_change = 0.0 + + # # Cumulative reward + # reward = r_action + r_obstacle + r_change + # return ( reward, terminal_state ) + +# Update Q-table values +def updateQTable(Q_table, state_ind, action, reward, next_state_ind, alpha, gamma): + if STATE_SPACE_IND_MIN <= state_ind <= STATE_SPACE_IND_MAX and STATE_SPACE_IND_MIN <= next_state_ind <= STATE_SPACE_IND_MAX: + status = 'updateQTable => OK' + Q_table[state_ind,action] = ( 1 - alpha ) * Q_table[state_ind,action] + alpha * ( reward + gamma * max(Q_table[next_state_ind,:]) ) + else: + status = 'updateQTable => INVALID STATE INDEX' + return ( Q_table, status ) diff --git a/scrpits/__pycache__/Control.cpython-310.pyc b/scrpits/__pycache__/Control.cpython-310.pyc index 587ec77..3fa52c1 100644 Binary files a/scrpits/__pycache__/Control.cpython-310.pyc and b/scrpits/__pycache__/Control.cpython-310.pyc differ diff --git a/scrpits/__pycache__/Lidar.cpython-310.pyc b/scrpits/__pycache__/Lidar.cpython-310.pyc index 40b318e..751b890 100644 Binary files a/scrpits/__pycache__/Lidar.cpython-310.pyc and b/scrpits/__pycache__/Lidar.cpython-310.pyc differ diff --git a/scrpits/__pycache__/Qlearning.cpython-310.pyc b/scrpits/__pycache__/Qlearning.cpython-310.pyc index b625864..86787e4 100644 Binary files a/scrpits/__pycache__/Qlearning.cpython-310.pyc and b/scrpits/__pycache__/Qlearning.cpython-310.pyc differ diff --git a/scrpits/control_node.py b/scrpits/control_node.py index 1940e3b..ae0c024 100644 --- a/scrpits/control_node.py +++ b/scrpits/control_node.py @@ -1,230 +1,271 @@ -from time import time -from time import sleep -from datetime import datetime -import matplotlib.pyplot as plt -import os -from rclpy.node import Node -import rclpy -from std_msgs.msg import String - -from geometry_msgs.msg import Twist - -import sys -cwd = os.getcwd() - -DATA_PATH = '/home/palm/PycharmProjects/ros2_ql/Data' -MODULES_PATH = '/home/palm/PycharmProjects/ros2_ql/scripts' -sys.path.insert(0, MODULES_PATH) - -from Qlearning import * -from Lidar import * -from Control import * -from gazebo_msgs.msg._model_state import ModelState - -from rclpy.impl.implementation_singleton import rclpy_implementation as _rclpy -from rclpy.node import Node -from rclpy.signals import SignalHandlerGuardCondition -from rclpy.utilities import timeout_sec_to_nsec - - -# Real robot -REAL_ROBOT = True - -# Action parameter -MIN_TIME_BETWEEN_ACTIONS = 0.0 - -# Initial and goal positions -INIT_POSITIONS_X = [ -0.7, -0.7, -0.5, -1, -2] -INIT_POSITIONS_Y = [ -0.7, 0.7, 1, -2, 1] -INIT_POSITIONS_THETA = [ 45, -45, -120, -90, 150] -GOAL_POSITIONS_X = [ 2.0, 2.0, 0.5, 1, 2] -GOAL_POSITIONS_Y = [ 1.0, -1.0, -1.9, 2, -1,] -GOAL_POSITIONS_THETA = [ 25.0, -40.0, -40, 60, -30,] - -PATH_IND = 4 - -# Initial & Goal position -if REAL_ROBOT: - X_INIT = 0.0 - Y_INIT = 0.0 - THETA_INIT = 0.0 - X_GOAL = 1.7 - Y_GOAL = 1.1 - THETA_GOAL = 90 -else: - RANDOM_INIT_POS = False - - X_INIT = INIT_POSITIONS_X[PATH_IND] - Y_INIT = INIT_POSITIONS_Y[PATH_IND] - THETA_INIT = INIT_POSITIONS_THETA[PATH_IND] - - X_GOAL = GOAL_POSITIONS_X[PATH_IND] - Y_GOAL = GOAL_POSITIONS_Y[PATH_IND] - THETA_GOAL = GOAL_POSITIONS_THETA[PATH_IND] - -# Log file directory - Q table source -Q_TABLE_SOURCE = DATA_PATH + '/Log_learning_FINAL' - -class ControlNode(Node): - def __init__(self): - super().__init__('control_node') - self.setPosPub = self.create_publisher(ModelState, 'gazebo/set_model_state', 10) - self.velPub = self.create_publisher(Twist, 'cmd_vel', 10) - self.actions = createActions() - self.state_space = createStateSpace() - self.Q_table = readQTable(Q_TABLE_SOURCE+'/Qtable.csv') - self.timer_period = .5 # seconds - self.timer = self.create_timer(self.timer_period, self.timer_callback) - self.t_step = self.get_clock().now() - self.robot_in_pos = False - self.count = 0 - - def wait_for_message( - node, - topic: str, - msg_type, - time_to_wait=-1 - ): - """ - Wait for the next incoming message. - :param msg_type: message type - :param node: node to initialize the subscription on - :param topic: topic name to wait for message - :time_to_wait: seconds to wait before returning - :return (True, msg) if a message was successfully received, (False, ()) if message - could not be obtained or shutdown was triggered asynchronously on the context. - """ - context = node.context - wait_set = _rclpy.WaitSet(1, 1, 0, 0, 0, 0, context.handle) - wait_set.clear_entities() - - sub = node.create_subscription(msg_type, topic, lambda _: None, 1) - wait_set.add_subscription(sub.handle) - sigint_gc = SignalHandlerGuardCondition(context=context) - wait_set.add_guard_condition(sigint_gc.handle) - - timeout_nsec = timeout_sec_to_nsec(time_to_wait) - wait_set.wait(timeout_nsec) - - subs_ready = wait_set.get_ready_entities('subscription') - guards_ready = wait_set.get_ready_entities('guard_condition') - - if guards_ready: - if sigint_gc.handle.pointer in guards_ready: - return (False, None) - - if subs_ready: - if sub.handle.pointer in subs_ready: - msg_info = sub.handle.take_message(sub.msg_type, sub.raw) - return (True, msg_info[0]) - - return (False, None) - - def timer_callback(self): - _, msgScan = self.wait_for_message('/scan', LaserScan) - _, odomMsg = self.wait_for_message('/odom', Odometry) - step_time = (self.get_clock().now() - self.t_step).nanoseconds / 1e9 - if step_time > MIN_TIME_BETWEEN_ACTIONS: - self.t_step = self.get_clock().now() - - if not self.robot_in_pos: - robotStop(self.velPub) - if REAL_ROBOT: - ( x_init , y_init , theta_init ) = (0, 0, 0) - _, odomMsg = self.wait_for_message('/odom', Odometry) - ( x , y ) = getPosition(odomMsg) - theta = degrees(getRotation(odomMsg)) - self.robot_in_pos = True - print('\r\nInitial position:') - print('x = %.2f [m]' % x) - print('y = %.2f [m]' % y) - print('theta = %.2f [degrees]' % theta) - print('') - else: - if RANDOM_INIT_POS: - ( x_init , y_init , theta_init ) = robotSetRandomPos(self.setPosPub) - else: - ( x_init , y_init , theta_init ) = robotSetPos(self.setPosPub, X_INIT, Y_INIT, THETA_INIT) - # check init pos - _, odomMsg = self.wait_for_message('/odom', Odometry) - ( x , y ) = getPosition(odomMsg) - theta = degrees(getRotation(odomMsg)) - print(theta, theta_init) - if abs(x-x_init) < 0.05 and abs(y-y_init) < 0.05 and abs(theta-theta_init) < 2: - self.robot_in_pos = True - print('\r\nInitial position:') - print('x = %.2f [m]' % x) - print('y = %.2f [m]' % y) - print('theta = %.2f [degrees]' % theta) - print('') - sleep(1) - else: - self.robot_in_pos = False - else: - self.count = self.count + 1 - text = '\r\nStep %d , Step time %.2f s' % (self.count, step_time) - - # Get robot position and orientation - ( x , y ) = getPosition(odomMsg) - theta = getRotation(odomMsg) - - # Get lidar scan - ( lidar, angles ) = lidarScan(msgScan) - ( state_ind, x1, x2 ,x3 ,x4 ) = scanDiscretization(self.state_space, lidar) - - # Check for objects nearby - crash = checkCrash(lidar) - object_nearby = checkObjectNearby(lidar) - goal_near = checkGoalNear(x, y, X_GOAL, Y_GOAL) - enable_feedback_control = True - - # Stop the simulation - if crash: - robotStop(self.velPub) - text = text + ' ==> Crash! End of simulation!' - status = 'Crash! End of simulation!' - raise SystemExit - # Feedback control algorithm - elif enable_feedback_control and ( not object_nearby or goal_near ): - status = robotFeedbackControl(self.velPub, x, y, theta, X_GOAL, Y_GOAL, radians(THETA_GOAL)) - text = text + ' ==> Feedback control algorithm ' - if goal_near: - text = text + '(goal near)' - # Q-learning algorithm - else: - ( action, status ) = getBestAction(self.Q_table, state_ind, self.actions) - if not status == 'getBestAction => OK': - print('\r\n', status, '\r\n') - - status = robotDoAction(self.velPub, action) - if not status == 'robotDoAction => OK': - print('\r\n', status, '\r\n') - text = text + ' ==> Q-learning algorithm' - - text = text + '\r\nx : %.2f -> %.2f [m]' % (x, X_GOAL) - text = text + '\r\ny : %.2f -> %.2f [m]' % (y, Y_GOAL) - text = text + '\r\ntheta : %.2f -> %.2f [degrees]' % (degrees(theta), THETA_GOAL) - - if status == 'Goal position reached!': - robotStop(self.velPub) - text = text + '\r\n\r\nGoal position reached! End of simulation!' - raise SystemExit - - print(text) - -def main(args=None): - rclpy.init(args=args) - - movebase_publisher = ControlNode() - try: - rclpy.spin(movebase_publisher) - except SystemExit: # <--- process the exception - rclpy.logging.get_logger("Quitting").info('Done') - # Destroy the node explicitly - # (optional - otherwise it will be done automatically - # when the garbage collector destroys the node object) - movebase_publisher.destroy_node() - rclpy.shutdown() - -if __name__ == '__main__': - main() +from time import time +from time import sleep +from datetime import datetime +import matplotlib.pyplot as plt +import os +from rclpy.node import Node +import rclpy +from std_msgs.msg import String + +from geometry_msgs.msg import Twist + +import argparse + +# Add args +parser = argparse.ArgumentParser(description='control node') +# Add gpos +parser.add_argument('--gpos', default = (0.0, 0.0), nargs=2, type=float, help='Enter x.x y.y pos for goal position') + +args = parser.parse_args() + + +import sys +cwd = os.getcwd() +print(cwd) +cwd = os.path.dirname(cwd) +print(cwd) + +# TODO: Change to proper PATH +DATA_PATH = os.path.join(cwd, 'Data') +MODULES_PATH = os.path.join(cwd, 'scrpits') +sys.path.insert(0, MODULES_PATH) + +from Qlearning import * +from Lidar import * +from Control import * +from gazebo_msgs.msg._model_state import ModelState + +from rclpy.impl.implementation_singleton import rclpy_implementation as _rclpy +from rclpy.node import Node +from rclpy.signals import SignalHandlerGuardCondition +from rclpy.utilities import timeout_sec_to_nsec + +# Real robot +REAL_ROBOT = True + +# Action parameter +MIN_TIME_BETWEEN_ACTIONS = 0.0 + +# Initial and goal positions +INIT_POSITIONS_X = [ -0.7, -0.7, -0.5, -1, -2] +INIT_POSITIONS_Y = [ -0.7, 0.7, 1, -2, 1] +INIT_POSITIONS_THETA = [ 45, -45, -120, -90, 150] +GOAL_POSITIONS_X = [ 2.0, 2.0, 0.5, 1, 2] +GOAL_POSITIONS_Y = [ 1.0, -1.0, -1.9, 2, -1,] +GOAL_POSITIONS_THETA = [ 25.0, -40.0, -40, 60, -30,] + +PATH_IND = 4 + +# Initial & Goal position +if REAL_ROBOT: + X_INIT = 0.0 + Y_INIT = 0.0 + THETA_INIT = 0.0 + X_GOAL = args.gpos[0] + Y_GOAL = args.gpos[1] + THETA_GOAL = 90 + GOAL_RADIUS = 0.1 +else: + RANDOM_INIT_POS = False + + X_INIT = INIT_POSITIONS_X[PATH_IND] + Y_INIT = INIT_POSITIONS_Y[PATH_IND] + THETA_INIT = INIT_POSITIONS_THETA[PATH_IND] + + X_GOAL = GOAL_POSITIONS_X[PATH_IND] + Y_GOAL = GOAL_POSITIONS_Y[PATH_IND] + THETA_GOAL = GOAL_POSITIONS_THETA[PATH_IND] + +# Log file directory - Q table source +Q_TABLE_SOURCE = DATA_PATH + '/Log_learning' + +''' +Add code +''' +# import pandas as pd + +# Get arguments from QTable +# n_actions_enable = len(qt.columns) + + +class ControlNode(Node): + def __init__(self): + print('init...') + super().__init__('control_node') + self.setPosPub = self.create_publisher(ModelState, 'gazebo/set_model_state', 10) + self.velPub = self.create_publisher(Twist, 'cmd_vel', 10) + # self.actions = createActions(n_actions_enable) + self.state_space = createStateSpace() + self.Q_table = readQTable(Q_TABLE_SOURCE+'/Qtable.csv') + # print(self.Q_table.shape) + # n_actions_enable = Q_table.shape[1] + self.actions = createActions(self.Q_table.shape[1]) + self.timer_period = .5 # seconds + self.timer = self.create_timer(self.timer_period, self.timer_callback) + self.t_step = self.get_clock().now() + self.robot_in_pos = False + self.count = 0 + + def wait_for_message( + node, + topic: str, + msg_type, + time_to_wait=-1 + ): + """ + Wait for the next incoming message. + :param msg_type: message type + :param node: node to initialize the subscription on + :param topic: topic name to wait for message + :time_to_wait: seconds to wait before returning + :return (True, msg) if a message was successfully received, (False, ()) if message + could not be obtained or shutdown was triggered asynchronously on the context. + """ + context = node.context + wait_set = _rclpy.WaitSet(1, 1, 0, 0, 0, 0, context.handle) + wait_set.clear_entities() + + sub = node.create_subscription(msg_type, topic, lambda _: None, 1) + wait_set.add_subscription(sub.handle) + sigint_gc = SignalHandlerGuardCondition(context=context) + wait_set.add_guard_condition(sigint_gc.handle) + + timeout_nsec = timeout_sec_to_nsec(time_to_wait) + wait_set.wait(timeout_nsec) + + subs_ready = wait_set.get_ready_entities('subscription') + guards_ready = wait_set.get_ready_entities('guard_condition') + + if guards_ready: + if sigint_gc.handle.pointer in guards_ready: + return (False, None) + + if subs_ready: + if sub.handle.pointer in subs_ready: + msg_info = sub.handle.take_message(sub.msg_type, sub.raw) + return (True, msg_info[0]) + + return (False, None) + + def timer_callback(self): + print('running...') + _, msgScan = self.wait_for_message('/scan', LaserScan) + _, odomMsg = self.wait_for_message('/odom', Odometry) + step_time = (self.get_clock().now() - self.t_step).nanoseconds / 1e9 + + if step_time > MIN_TIME_BETWEEN_ACTIONS: + self.t_step = self.get_clock().now() + + if not self.robot_in_pos: + robotStop(self.velPub) + if REAL_ROBOT: + ( x_init , y_init , theta_init ) = (0, 0, 0) + _, odomMsg = self.wait_for_message('/odom', Odometry) + ( x , y ) = getPosition(odomMsg) + self.prev_position = getPosition(odomMsg) + self.MAX_RADIUS = np.linalg.norm([x_init - X_GOAL, y_init - Y_GOAL]) + theta = degrees(getRotation(odomMsg)) + self.robot_in_pos = True + print('\r\nInitial position:') + print('x = %.2f [m]' % x) + print('y = %.2f [m]' % y) + print('theta = %.2f [degrees]' % theta) + print('') + print('\r\nGoal position:') + print('x = %.2f [m]' % X_GOAL) + print('y = %.2f [m]' % Y_GOAL) + print('') + input('Press Enter to start nong...') + else: + if RANDOM_INIT_POS: + ( x_init , y_init , theta_init ) = robotSetRandomPos(self.setPosPub) + else: + ( x_init , y_init , theta_init ) = robotSetPos(self.setPosPub, X_INIT, Y_INIT, THETA_INIT) + # check init pos + _, odomMsg = self.wait_for_message('/odom', Odometry) + ( x , y ) = getPosition(odomMsg) + theta = degrees(getRotation(odomMsg)) + print(theta, theta_init) + if abs(x-x_init) < 0.05 and abs(y-y_init) < 0.05 and abs(theta-theta_init) < 2: + self.robot_in_pos = True + print('\r\nInitial position:') + print('x = %.2f [m]' % x) + print('y = %.2f [m]' % y) + print('theta = %.2f [degrees]' % theta) + print('') + sleep(1) + else: + self.robot_in_pos = False + else: + self.count = self.count + 1 + text = '\r\nStep %d , Step time %.2f s' % (self.count, step_time) + + # Get robot position and orientation + ( x , y ) = getPosition(odomMsg) + theta = getRotation(odomMsg) + + # Get lidar scan + ( lidar, angles ) = lidarScan(msgScan) + # ( state_ind, x1, x2, x3 , x4 , x5, x6, x7 ) = scanDiscretization(self.state_space, lidar) + ( state_ind, x1, x2, x3 , x4 , x5, x6, x7, x8, x9, x10 ) = scanDiscretization(self.state_space, lidar, (X_GOAL, Y_GOAL), (x, y), self.prev_position, self.MAX_RADIUS, GOAL_RADIUS) + # ( state_ind, x1, x2, x3 , x4 , x5, x6, x7, x8, x9 ) = scanDiscretization(self.state_space, lidar, (X_GOAL, Y_GOAL), (x, y), self.prev_position, self.MAX_RADIUS, GOAL_RADIUS) + + + # Check for objects nearby + crash = checkCrash(lidar) + object_nearby = checkObjectNearby(lidar) + goal_near = checkGoalNear(x, y, X_GOAL, Y_GOAL) + enable_feedback_control = True + + # Stop the simulation + if crash: + robotStop(self.velPub) + text = text + ' ==> Crash! End of simulation!' + status = 'Crash! End of simulation!' + raise SystemExit + # Feedback control algorithm + elif enable_feedback_control and ( not object_nearby or goal_near ): + status = robotFeedbackControl(self.velPub, x, y, theta, X_GOAL, Y_GOAL, radians(THETA_GOAL)) + text = text + ' ==> Feedback control algorithm ' + if goal_near: + text = text + '(goal near)' + # Q-learning algorithm + else: + ( action, status ) = getBestAction(self.Q_table, state_ind, self.actions) + if not status == 'getBestAction => OK': + print('\r\n', status, '\r\n') + + status = robotDoAction(self.velPub, action) + if not status == 'robotDoAction => OK': + print('\r\n', status, '\r\n') + text = text + ' ==> Q-learning algorithm' + + text = text + '\r\nx : %.2f -> %.2f [m]' % (x, X_GOAL) + text = text + '\r\ny : %.2f -> %.2f [m]' % (y, Y_GOAL) + text = text + '\r\ntheta : %.2f -> %.2f [degrees]' % (degrees(theta), THETA_GOAL) + + self.prev_position = ( x , y ) + + if status == 'Goal position reached!': + robotStop(self.velPub) + text = text + '\r\n\r\nGoal position reached! End of simulation!' + raise SystemExit + + print(text) + +def main(args=None): + rclpy.init(args=args) + + movebase_publisher = ControlNode() + try: + rclpy.spin(movebase_publisher) + except SystemExit: # <--- process the exception + rclpy.logging.get_logger("Quitting").info('Done') + # Destroy the node explicitly + # (optional - otherwise it will be done automatically + # when the garbage collector destroys the node object) + movebase_publisher.destroy_node() + rclpy.shutdown() + +if __name__ == '__main__': + main() diff --git a/scrpits/learning_node.py b/scrpits/learning_node.py index 33fb22b..a42fe5a 100644 --- a/scrpits/learning_node.py +++ b/scrpits/learning_node.py @@ -1,402 +1,599 @@ -#! /usr/bin/env python - -import rclpy -from time import time -from time import sleep -from datetime import datetime -import matplotlib.pyplot as plt -from rclpy.node import Node -from std_srvs.srv import Empty -from std_srvs.srv._empty import Empty_Request -import sys -DATA_PATH = '/home/palm/PycharmProjects/ros2_ql/Data' -MODULES_PATH = '/home/palm/PycharmProjects/ros2_ql/scripts' -sys.path.insert(0, MODULES_PATH) -from gazebo_msgs.msg._model_state import ModelState -from geometry_msgs.msg import Twist - -from Qlearning import * -from Lidar import * -from Control import * -from rclpy.impl.implementation_singleton import rclpy_implementation as _rclpy -from rclpy.node import Node -from rclpy.signals import SignalHandlerGuardCondition -from rclpy.utilities import timeout_sec_to_nsec - -# Episode parameters -MAX_EPISODES = 400 -MAX_STEPS_PER_EPISODE = 500 -MIN_TIME_BETWEEN_ACTIONS = 0.0 - -# Learning parameters -ALPHA = 0.5 -GAMMA = 0.9 - -T_INIT = 25 -T_GRAD = 0.95 -T_MIN = 0.001 - -EPSILON_INIT = 0.9 -EPSILON_GRAD = 0.96 -EPSILON_MIN = 0.05 - -# 1 - Softmax , 2 - Epsilon greedy -EXPLORATION_FUNCTION = 1 - -# Initial position -X_INIT = -2.0 -Y_INIT = -0.5 -THETA_INIT = 0.0 - -RANDOM_INIT_POS = False - -# Log file directory -LOG_FILE_DIR = DATA_PATH + '/Log_learning' - -# Q table source file -Q_SOURCE_DIR = '' - -class LearningNode(Node): - def __init__(self): - super().__init__('learning_node') - self.timer_period = .5 # seconds - self.timer = self.create_timer(self.timer_period, self.timer_callback) - self.reset = self.create_client(Empty, '/reset_simulation') - self.setPosPub = self.create_publisher(ModelState, 'gazebo/set_model_state', 10) - self.velPub = self.create_publisher(Twist, 'cmd_vel', 10) - self.dummy_req = Empty_Request() - self.reset.call_async(self.dummy_req) - self.actions = createActions() - self.state_space = createStateSpace() - self.Q_table = createQTable(len(self.state_space), len(self.actions)) - print('Initial Q-table:') - print(self.Q_table) - # Init log files - self.log_sim_info = open(LOG_FILE_DIR+'/LogInfo.txt','w+') - self.log_sim_params = open(LOG_FILE_DIR+'/LogParams.txt','w+') - # Learning parameters - self.T = T_INIT - self.EPSILON = EPSILON_INIT - self.alpha = ALPHA - self.gamma = GAMMA - # Episodes, steps, rewards - self.ep_steps = 0 - self.ep_reward = 0 - self.episode = 1 - self.crash = 0 - self.reward_max_per_episode = np.array([]) - self.reward_min_per_episode = np.array([]) - self.reward_avg_per_episode = np.array([]) - self.ep_reward_arr = np.array([]) - self.steps_per_episode = np.array([]) - self.reward_per_episode = np.array([]) - # initial position - self.robot_in_pos = False - self.first_action_taken = False - # init time - self.t_0 = self.get_clock().now() - self.t_start = self.get_clock().now() - - # init timer - while not (self.t_start > self.t_0): - self.t_start = self.get_clock().now() - - self.t_ep = self.t_start - self.t_sim_start = self.t_start - self.t_step = self.t_start - - self.T_per_episode = np.array([]) - self.EPSILON_per_episode = np.array([]) - self.t_per_episode = np.array([]) - - def log_init(self): - # Date - now_start = datetime.now() - dt_string_start = now_start.strftime("%d/%m/%Y %H:%M:%S") - - # Log date to files - text = '\r\n' + 'SIMULATION START ==> ' + dt_string_start + '\r\n\r\n' - print(text) - self.log_sim_info.write(text) - self.log_sim_params.write(text) - - # Log simulation parameters - text = '\r\nSimulation parameters: \r\n' - text = text + '--------------------------------------- \r\n' - if RANDOM_INIT_POS: - text = text + 'INITIAL POSITION = RANDOM \r\n' - else: - text = text + 'INITIAL POSITION = ( %.2f , %.2f , %.2f ) \r\n' % (X_INIT,Y_INIT,THETA_INIT) - text = text + '--------------------------------------- \r\n' - text = text + 'MAX_EPISODES = %d \r\n' % MAX_EPISODES - text = text + 'MAX_STEPS_PER_EPISODE = %d \r\n' % MAX_STEPS_PER_EPISODE - text = text + 'MIN_TIME_BETWEEN_ACTIONS = %.2f s \r\n' % MIN_TIME_BETWEEN_ACTIONS - text = text + '--------------------------------------- \r\n' - text = text + 'ALPHA = %.2f \r\n' % ALPHA - text = text + 'GAMMA = %.2f \r\n' % GAMMA - if EXPLORATION_FUNCTION == 1: - text = text + 'T_INIT = %.3f \r\n' % T_INIT - text = text + 'T_GRAD = %.3f \r\n' % T_GRAD - text = text + 'T_MIN = %.3f \r\n' % T_MIN - else: - text = text + 'EPSILON_INIT = %.3f \r\n' % EPSILON_INIT - text = text + 'EPSILON_GRAD = %.3f \r\n' % EPSILON_GRAD - text = text + 'EPSILON_MIN = %.3f \r\n' % EPSILON_MIN - text = text + '--------------------------------------- \r\n' - text = text + 'MAX_LIDAR_DISTANCE = %.2f \r\n' % MAX_LIDAR_DISTANCE - text = text + 'COLLISION_DISTANCE = %.2f \r\n' % COLLISION_DISTANCE - text = text + 'ZONE_0_LENGTH = %.2f \r\n' % ZONE_0_LENGTH - text = text + 'ZONE_1_LENGTH = %.2f \r\n' % ZONE_1_LENGTH - text = text + '--------------------------------------- \r\n' - text = text + 'CONST_LINEAR_SPEED_FORWARD = %.3f \r\n' % CONST_LINEAR_SPEED_FORWARD - text = text + 'CONST_ANGULAR_SPEED_FORWARD = %.3f \r\n' % CONST_ANGULAR_SPEED_FORWARD - text = text + 'CONST_LINEAR_SPEED_TURN = %.3f \r\n' % CONST_LINEAR_SPEED_TURN - text = text + 'CONST_ANGULAR_SPEED_TURN = %.3f \r\n' % CONST_ANGULAR_SPEED_TURN - self.log_sim_params.write(text) - - def wait_for_message( - node, - topic: str, - msg_type, - time_to_wait=-1 - ): - """ - Wait for the next incoming message. - :param msg_type: message type - :param node: node to initialize the subscription on - :param topic: topic name to wait for message - :time_to_wait: seconds to wait before returning - :return (True, msg) if a message was successfully received, (False, ()) if message - could not be obtained or shutdown was triggered asynchronously on the context. - """ - context = node.context - wait_set = _rclpy.WaitSet(1, 1, 0, 0, 0, 0, context.handle) - wait_set.clear_entities() - - sub = node.create_subscription(msg_type, topic, lambda _: None, 1) - wait_set.add_subscription(sub.handle) - sigint_gc = SignalHandlerGuardCondition(context=context) - wait_set.add_guard_condition(sigint_gc.handle) - - timeout_nsec = timeout_sec_to_nsec(time_to_wait) - wait_set.wait(timeout_nsec) - - subs_ready = wait_set.get_ready_entities('subscription') - guards_ready = wait_set.get_ready_entities('guard_condition') - - if guards_ready: - if sigint_gc.handle.pointer in guards_ready: - return (False, None) - - if subs_ready: - if sub.handle.pointer in subs_ready: - msg_info = sub.handle.take_message(sub.msg_type, sub.raw) - return (True, msg_info[0]) - - return (False, None) - - def timer_callback(self): - _, msgScan = self.wait_for_message('/scan', LaserScan) - step_time = (self.get_clock().now() - self.t_step).nanoseconds / 1e9 - if step_time > MIN_TIME_BETWEEN_ACTIONS: - self.t_step = self.get_clock().now() - if step_time > 2: - text = '\r\nTOO BIG STEP TIME: %.2f s' % step_time - print(text) - self.log_sim_info.write(text+'\r\n') - - # End of Learning - if self.episode > MAX_EPISODES: - # simulation time - sim_time = (self.get_clock().now() - self.t_sim_start).nanoseconds / 1e9 - sim_time_h = sim_time // 3600 - sim_time_m = ( sim_time - sim_time_h * 3600 ) // 60 - sim_time_s = sim_time - sim_time_h * 3600 - sim_time_m * 60 - - # real time - now_stop = datetime.now() - dt_string_stop = now_stop.strftime("%d/%m/%Y %H:%M:%S") - real_time_delta = (now_stop - self.now_start).total_seconds() - real_time_h = real_time_delta // 3600 - real_time_m = ( real_time_delta - real_time_h * 3600 ) // 60 - real_time_s = real_time_delta - real_time_h * 3600 - real_time_m * 60 - - # Log learning session info to file - text = '--------------------------------------- \r\n\r\n' - text = text + 'MAX EPISODES REACHED(%d), LEARNING FINISHED ==> ' % MAX_EPISODES + dt_string_stop + '\r\n' - text = text + 'Simulation time: %d:%d:%d h/m/s \r\n' % (sim_time_h, sim_time_m, sim_time_s) - text = text + 'Real time: %d:%d:%d h/m/s \r\n' % (real_time_h, real_time_m, real_time_s) - print(text) - self.log_sim_info.write('\r\n'+text+'\r\n') - self.log_sim_params.write(text+'\r\n') - # Log data to file - saveQTable(LOG_FILE_DIR+'/Qtable.csv', self.Q_table) - np.savetxt(LOG_FILE_DIR+'/StateSpace.csv', self.state_space, '%d') - np.savetxt(LOG_FILE_DIR+'/steps_per_episode.csv', self.steps_per_episode, delimiter = ' , ') - np.savetxt(LOG_FILE_DIR+'/reward_per_episode.csv', self.reward_per_episode, delimiter = ' , ') - np.savetxt(LOG_FILE_DIR+'/T_per_episode.csv', self.T_per_episode, delimiter = ' , ') - np.savetxt(LOG_FILE_DIR+'/EPSILON_per_episode.csv', self.EPSILON_per_episode, delimiter = ' , ') - np.savetxt(LOG_FILE_DIR+'/reward_min_per_episode.csv', self.reward_min_per_episode, delimiter = ' , ') - np.savetxt(LOG_FILE_DIR+'/reward_max_per_episode.csv', self.reward_max_per_episode, delimiter = ' , ') - np.savetxt(LOG_FILE_DIR+'/reward_avg_per_episode.csv', self.reward_avg_per_episode, delimiter = ' , ') - np.savetxt(LOG_FILE_DIR+'/t_per_episode.csv', self.t_per_episode, delimiter = ' , ') - - # Close files and shut down node - self.log_sim_info.close() - self.log_sim_params.close() - raise SystemExit - else: - ep_time = (self.get_clock().now() - self.t_ep).nanoseconds / 1e9 - # End of en Episode - if self.crash or self.ep_steps >= MAX_STEPS_PER_EPISODE: - robotStop(self.velPub) - if self.crash: - # get crash position - _, odomMsg = self.wait_for_message('/odom', Odometry) - ( x_crash , y_crash ) = getPosition(odomMsg) - theta_crash = degrees(getRotation(odomMsg)) - - self.t_ep = self.get_clock().now() - self.reward_min = np.min(self.ep_reward_arr) - self.reward_max = np.max(self.ep_reward_arr) - self.reward_avg = np.mean(self.ep_reward_arr) - now = datetime.now() - dt_string = now.strftime("%d/%m/%Y %H:%M:%S") - text = '---------------------------------------\r\n' - if self.crash: - text = text + '\r\nEpisode %d ==> CRASH {%.2f,%.2f,%.2f} ' % (self.episode, x_crash, y_crash, theta_crash) + dt_string - self.reset.call_async(self.dummy_req) - elif self.ep_steps >= MAX_STEPS_PER_EPISODE: - text = text + '\r\nEpisode %d ==> MAX STEPS PER EPISODE REACHED {%d} ' % (self.episode, MAX_STEPS_PER_EPISODE) + dt_string - else: - text = text + '\r\nEpisode %d ==> UNKNOWN TERMINAL CASE ' % self.episode + dt_string - text = text + '\r\nepisode time: %.2f s (avg step: %.2f s) \r\n' % (ep_time, ep_time / self.ep_steps) - text = text + 'episode steps: %d \r\n' % self.ep_steps - text = text + 'episode reward: %.2f \r\n' % self.ep_reward - text = text + 'episode max | avg | min reward: %.2f | %.2f | %.2f \r\n' % (self.reward_max, self.reward_avg, self.reward_min) - if EXPLORATION_FUNCTION == 1: - text = text + 'T = %f \r\n' % self.T - else: - text = text + 'EPSILON = %f \r\n' % self.EPSILON - print(text) - self.log_sim_info.write('\r\n'+text) - - self.steps_per_episode = np.append(self.steps_per_episode, self.ep_steps) - self.reward_per_episode = np.append(self.reward_per_episode, self.ep_reward) - self.T_per_episode = np.append(self.T_per_episode, self.T) - self.EPSILON_per_episode = np.append(self.EPSILON_per_episode, self.EPSILON) - self.t_per_episode = np.append(self.t_per_episode, ep_time) - self.reward_min_per_episode = np.append(self.reward_min_per_episode, self.reward_min) - self.reward_max_per_episode = np.append(self.reward_max_per_episode, self.reward_max) - self.reward_avg_per_episode = np.append(self.reward_avg_per_episode, self.reward_avg) - self.ep_reward_arr = np.array([]) - self.ep_steps = 0 - self.ep_reward = 0 - self.crash = 0 - self.robot_in_pos = False - self.first_action_taken = False - if self.T > T_MIN: - self.T = T_GRAD * self.T - if self.EPSILON > EPSILON_MIN: - self.EPSILON = EPSILON_GRAD * self.EPSILON - self.episode = self.episode + 1 - else: - self.ep_steps = self.ep_steps + 1 - # Initial position - if not self.robot_in_pos: - robotStop(self.velPub) - self.ep_steps = self.ep_steps - 1 - self.first_action_taken = False - # init pos - if RANDOM_INIT_POS: - ( x_init , y_init , theta_init ) = robotSetRandomPos(self.setPosPub) - else: - ( x_init , y_init , theta_init ) = robotSetPos(self.setPosPub, X_INIT, Y_INIT, THETA_INIT) - - _, odomMsg = self.wait_for_message('/odom', Odometry) - ( x , y ) = getPosition(odomMsg) - theta = degrees(getRotation(odomMsg)) - # check init pos - if abs(x-x_init) < 0.01 and abs(y-y_init) < 0.01 and abs(theta-theta_init) < 1: - self.robot_in_pos = True - #sleep(2) - else: - self.robot_in_pos = False - # First acion - elif not self.first_action_taken: - ( lidar, angles ) = lidarScan(msgScan) - ( state_ind, x1, x2 ,x3 ,x4 ) = scanDiscretization(self.state_space, lidar) - self.crash = checkCrash(lidar) - - if EXPLORATION_FUNCTION == 1 : - ( self.action, status_strat ) = softMaxSelection(self.Q_table, state_ind, self.actions, self.T) - else: - ( self.action, status_strat ) = epsiloGreedyExploration(self.Q_table, state_ind, self.actions, self.T) - - status_rda = robotDoAction(self.velPub, self.action) - - self.prev_lidar = lidar - self.prev_action = self.action - self.prev_state_ind = state_ind - - self.first_action_taken = True - - if not (status_strat == 'softMaxSelection => OK' or status_strat == 'epsiloGreedyExploration => OK'): - print('\r\n', status_strat, '\r\n') - self.log_sim_info.write('\r\n'+status_strat+'\r\n') - - if not status_rda == 'robotDoAction => OK': - print('\r\n', status_rda, '\r\n') - self.log_sim_info.write('\r\n'+status_rda+'\r\n') - - # Rest of the algorithm - else: - ( lidar, angles ) = lidarScan(msgScan) - ( state_ind, x1, x2 ,x3 ,x4 ) = scanDiscretization(self.state_space, lidar) - self.crash = checkCrash(lidar) - - ( reward, terminal_state ) = getReward(self.action, self.prev_action, lidar, self.prev_lidar, self.crash) - - ( self.Q_table, status_uqt ) = updateQTable(self.Q_table, self.prev_state_ind, self.action, reward, state_ind, self.alpha, self.gamma) - - if EXPLORATION_FUNCTION == 1: - ( self.action, status_strat ) = softMaxSelection(self.Q_table, state_ind, self.actions, self.T) - else: - ( self.action, status_strat ) = epsiloGreedyExploration(self.Q_table, state_ind, self.actions, self.T) - - status_rda = robotDoAction(self.velPub, self.action) - - if not status_uqt == 'updateQTable => OK': - print('\r\n', status_uqt, '\r\n') - self.log_sim_info.write('\r\n'+status_uqt+'\r\n') - if not (status_strat == 'softMaxSelection => OK' or status_strat == 'epsiloGreedyExploration => OK'): - print('\r\n', status_strat, '\r\n') - self.log_sim_info.write('\r\n'+status_strat+'\r\n') - if not status_rda == 'robotDoAction => OK': - print('\r\n', status_rda, '\r\n') - self.log_sim_info.write('\r\n'+status_rda+'\r\n') - - self.ep_reward = self.ep_reward + reward - self.ep_reward_arr = np.append(self.ep_reward_arr, reward) - self.prev_lidar = lidar - self.prev_action = self.action - self.prev_state_ind = state_ind - - - -def main(args=None): - rclpy.init(args=args) - - movebase_publisher = LearningNode() - try: - rclpy.spin(movebase_publisher) - except SystemExit: # <--- process the exception - rclpy.logging.get_logger("End of learning").info('Done') - # Destroy the node explicitly - # (optional - otherwise it will be done automatically - # when the garbage collector destroys the node object) - movebase_publisher.destroy_node() - rclpy.shutdown() - -if __name__ == '__main__': - main() +#! /usr/bin/env python + +import rclpy +from time import time +from time import sleep +# import time +from datetime import datetime +import matplotlib.pyplot as plt +from rclpy.node import Node +from std_srvs.srv import Empty +import pandas as pd +from std_srvs.srv._empty import Empty_Request +import sys +DATA_PATH = '/mnt/c/Users/keera/Documents/Github/Basic_robot/QtableV1/Data' +MODULES_PATH = '/mnt/c/Users/keera/Documents/Github/Basic_robot/QtableV1/scrpits' + +sys.path.insert(0, MODULES_PATH) +from gazebo_msgs.msg._model_state import ModelState +from geometry_msgs.msg import Twist + +from Qlearning import * +from Lidar import * +from Control import * +from rclpy.impl.implementation_singleton import rclpy_implementation as _rclpy +from rclpy.node import Node +from rclpy.signals import SignalHandlerGuardCondition +from rclpy.utilities import timeout_sec_to_nsec + + + +import argparse +import os +# Episode parameters +MAX_EPISODES = 10 +MAX_STEPS_PER_EPISODE = 500 +MIN_TIME_BETWEEN_ACTIONS = 0.0 +MAX_EPISODES_BEFORE_SAVE = 5 + +# Learning parameters +ALPHA = 0.5 +GAMMA = 0.9 + +T_INIT = 25 +T_GRAD = 0.95 +T_MIN = 0.001 + +EPSILON_INIT = 0.5 +EPSILON_GRAD = 0.96 +EPSILON_MIN = 0.05 + +# 1 - Softmax , 2 - Epsilon greedy +EXPLORATION_FUNCTION = 1 + +# Initial position +# X_INIT = -2.0 +# Y_INIT = -0.0 +THETA_INIT = 0.0 + +RANDOM_INIT_POS = False + +# Log file directory +LOG_FILE_DIR = DATA_PATH + '/Log_learning' + +# Q table source file +Q_SOURCE_DIR = LOG_FILE_DIR + '/Qtable.csv' +Q_BEST_SOURCE_DIR = LOG_FILE_DIR + '/Qtable_best_time.csv' + +RADIUS_REDUCE_RATE = .5 +REWARD_THRESHOLD = -200 +CUMULATIVE_REWARD = 0.0 + +GOAL_POSITION = (0., 2., .0) + +GOAL_X = 0 +GOAL_Y = 0 +GOAL_RADIUS = .1 + +# edit when chang order in def roboDoAction in Control.py ***** +ACTIONS_DESCRIPTION = { 0 : 'Forward', + 1 : 'CW', + 2 : 'CCW', + 3 : 'Stop', + 4 : 'SuperForward'} +MAX_WIDTH = 25 + +parser = argparse.ArgumentParser(description='Qtable V1 ~~Branch: welcomeToV2') +# Log file directory +parser.add_argument('--log_file_dir', default = LOG_FILE_DIR, type=str, help='/Data/Log_learning') +# Q table source file +parser.add_argument('--Q_source_dir', default = Q_SOURCE_DIR, type=str, help='/Data/Log_learning/Qtable.csv') +# Q table best source file +# parser.add_argument('--Q_best_source_dir', default = Q_BEST_SOURCE_DIR, type=str, help='/Data/Log_learning/Qtable_best_time.csv') + +# Episode parameters +parser.add_argument('--max_episodes', default=MAX_EPISODES, type=int, help="MAX_EPISODES = 10 (default)", dest="max_episodes") +parser.add_argument('--max_step_per_episodes', default=MAX_STEPS_PER_EPISODE, type=int, help="MAX_STEPS_PER_EPISODE = 500 (default)") +parser.add_argument('--max_episodes_before_save', default=MAX_EPISODES_BEFORE_SAVE, type=int, help="MAX_EPISODES_BEFORE_SAVE = 5 (default)") + +# Learning parameters +parser.add_argument('--exploration_func', default=1, type=int, choices=[1, 2], help="# 1 - Softmax(default) , 2 - Epsilon greedy") + +# need to use action='store true' to store boolean. True when type --resume. False otherwise +# parser.add_argument('--get_best', action='store_true', help ="save Qtable_best when minimize rel_time --> True | False") +parser.add_argument('--resume', action='store_true', help ="continue learning with same Qtable--> True | False") +parser.add_argument('--n_actions_enable', default=5, type=int, help='default--> 0:forward, 1:CW, 2:CCW, 3:stop, 4:superForward') + +parser.add_argument('--radiaus_reduce_rate', default=RADIUS_REDUCE_RATE, type=float) +parser.add_argument('--reward_threshold', default=REWARD_THRESHOLD, type=int) +parser.add_argument('--GOAL_POSITION', default=GOAL_POSITION, nargs='+', type=float) +parser.add_argument('--GOAL_RADIUS', default=GOAL_RADIUS, type=float) +parser.add_argument('--RANDOM_GOAL', default=0, type=int, dest="RANDOM_GOAL") + + +args_parse = parser.parse_args() +is_rand = args_parse.RANDOM_GOAL + +import random + +if is_rand: + R = [-2,2] + (GOAL_X, GOAL_Y) = np.random.choice(R,2) #random from cartesia product of R + # R_new = [(2,2),(2,-2),(-2,-2)] + # print(R_new) + # GOAL_X = random.choice(R_new)[0] + # GOAL_Y = random.choice(R_new)[1] + GOAL_THETA = 0 + print((GOAL_X, GOAL_Y)) +else: + (GOAL_X, GOAL_Y, GOAL_THETA) = tuple(args_parse.GOAL_POSITION) + +WIN_COUNT = 0.0 + + +class LearningNode(Node): + def __init__(self): + super().__init__('learning_node') + self.timer_period = .5 # seconds + self.timer = self.create_timer(self.timer_period, self.timer_callback) + self.reset = self.create_client(Empty, '/reset_simulation') + self.setPosPub = self.create_publisher(ModelState, 'gazebo/set_model_state', 10) + self.velPub = self.create_publisher(Twist, 'cmd_vel', 10) + self.dummy_req = Empty_Request() + self.reset.call_async(self.dummy_req) + self.actions = createActions(args_parse.n_actions_enable) + self.state_space = createStateSpace() + self.GOAL_X = GOAL_X + self.GOAL_Y = GOAL_Y + print(f'\n {"start learning_node with":^{MAX_WIDTH*4}}') + print('-'*100) + for arg in vars(args_parse): + print(f'{arg:<{MAX_WIDTH}}: {str(getattr(args_parse, arg)):<{MAX_WIDTH}}') + + print('-'*100) + print(f'\n state_space shape: {self.state_space.shape[0]}') + print(f'\n n_actions: {args_parse.n_actions_enable} --> {[ACTIONS_DESCRIPTION[i] for i in range(args_parse.n_actions_enable)]}') + + + if args_parse.resume: + # raise SystemExit + self.Q_table = pd.read_csv(args_parse.Q_source_dir, header=None) + self.Q_table = self.Q_table.to_numpy() + else: + print(f'\n not resume then create new Q Table') + self.Q_table = createQTable(len(self.state_space), len(self.actions)) + print(f' Q-table shape{self.Q_table.shape}') + # if not os.path.exists(args_parse.log_file_dir +'/LogInfo.txt'): + # self.log_sim_info = open(args_parse.log_file_dir +'/LogInfo.txt','w') + # if not os.path.exists(args_parse.log_file_dir +'/LogInfo.txt'): + # self.log_sim_params = open(args_parse.log_file_dir +'/LogParams.txt','w') + # Init log files + + + print() + # input("Press enter to continue...") + self.log_sim_info = open(args_parse.log_file_dir +'/LogInfo.txt','w') + self.log_sim_params = open(args_parse.log_file_dir +'/LogParams.txt','w') + # Learning parameters + self.T = T_INIT + self.EPSILON = EPSILON_INIT + self.alpha = ALPHA + self.gamma = GAMMA + # Episodes, steps, rewards + self.ep_steps = 0 + self.ep_reward = 0 + self.episode = 1 + self.crash = 0 + self.reward_max_per_episode = np.array([]) + self.reward_min_per_episode = np.array([]) + self.reward_avg_per_episode = np.array([]) + self.ep_reward_arr = np.array([]) + self.steps_per_episode = np.array([]) + self.reward_per_episode = np.array([]) + # initial position + self.robot_in_pos = False + self.first_action_taken = False + + # init time + self.t_0 = self.get_clock().now() + self.t_start = self.get_clock().now() + + # init timer + while not (self.t_start > self.t_0): + self.t_start = self.get_clock().now() + + self.t_ep = self.t_start + self.t_sim_start = self.t_start + self.t_step = self.t_start + + self.T_per_episode = np.array([]) + self.EPSILON_per_episode = np.array([]) + self.t_per_episode = np.array([]) + + self.CUMULATIVE_REWARD = CUMULATIVE_REWARD + self.WIN_COUNT = WIN_COUNT + self.terminal_state = False + self.is_set_pos = False + + def log_init(self): + # Date + now_start = datetime.now() + dt_string_start = now_start.strftime("%d/%m/%Y %H:%M:%S") + + # Log date to files + text = '\r\n' + 'SIMULATION START ==> ' + dt_string_start + '\r\n\r\n' + print(text) + self.log_sim_info.write(text) + self.log_sim_params.write(text) + + # Log simulation parameters + text = '\r\nSimulation parameters: \r\n' + text = text + '--------------------------------------- \r\n' + # if RANDOM_INIT_POS: + # text = text + 'INITIAL POSITION = RANDOM \r\n' + # else: + # text = text + 'INITIAL POSITION = ( %.2f , %.2f , %.2f ) \r\n' % (X_INIT,Y_INIT,THETA_INIT) + text = text + '--------------------------------------- \r\n' + text = text + 'MAX_EPISODES = %d \r\n' % args_parse.max_episodes + text = text + 'MAX_STEPS_PER_EPISODE = %d \r\n' % args_parse.max_step_per_episodes + text = text + 'MIN_TIME_BETWEEN_ACTIONS = %.2f s \r\n' % MIN_TIME_BETWEEN_ACTIONS + text = text + '--------------------------------------- \r\n' + text = text + 'ALPHA = %.2f \r\n' % ALPHA + text = text + 'GAMMA = %.2f \r\n' % GAMMA + if args_parse.exploration_func == 1: + text = text + 'T_INIT = %.3f \r\n' % T_INIT + text = text + 'T_GRAD = %.3f \r\n' % T_GRAD + text = text + 'T_MIN = %.3f \r\n' % T_MIN + else: + text = text + 'EPSILON_INIT = %.3f \r\n' % EPSILON_INIT + text = text + 'EPSILON_GRAD = %.3f \r\n' % EPSILON_GRAD + text = text + 'EPSILON_MIN = %.3f \r\n' % EPSILON_MIN + text = text + '--------------------------------------- \r\n' + text = text + 'MAX_LIDAR_DISTANCE = %.2f \r\n' % MAX_LIDAR_DISTANCE + text = text + 'COLLISION_DISTANCE = %.2f \r\n' % COLLISION_DISTANCE + text = text + 'ZONE_0_LENGTH = %.2f \r\n' % ZONE_0_LENGTH + text = text + 'ZONE_1_LENGTH = %.2f \r\n' % ZONE_1_LENGTH + text = text + '--------------------------------------- \r\n' + text = text + 'CONST_LINEAR_SPEED_FORWARD = %.3f \r\n' % CONST_LINEAR_SPEED_FORWARD + text = text + 'CONST_ANGULAR_SPEED_FORWARD = %.3f \r\n' % CONST_ANGULAR_SPEED_FORWARD + text = text + 'CONST_LINEAR_SPEED_TURN = %.3f \r\n' % CONST_LINEAR_SPEED_TURN + text = text + 'CONST_ANGULAR_SPEED_TURN = %.3f \r\n' % CONST_ANGULAR_SPEED_TURN + self.log_sim_params.write(text) + + def wait_for_message( + node, + topic: str, + msg_type, + time_to_wait=-1 + ): + """ + Wait for the next incoming message. + :param msg_type: message type + :param node: node to initialize the subscription on + :param topic: topic name to wait for message + :time_to_wait: seconds to wait before returning + :return (True, msg) if a message was successfully received, (False, ()) if message + could not be obtained or shutdown was triggered asynchronously on the context. + """ + context = node.context + wait_set = _rclpy.WaitSet(1, 1, 0, 0, 0, 0, context.handle) + wait_set.clear_entities() + + sub = node.create_subscription(msg_type, topic, lambda _: None, 1) + wait_set.add_subscription(sub.handle) + sigint_gc = SignalHandlerGuardCondition(context=context) + wait_set.add_guard_condition(sigint_gc.handle) + + timeout_nsec = timeout_sec_to_nsec(time_to_wait) + # print(f'time_to_wait: {timeout_nsec}') + wait_set.wait(timeout_nsec) + + subs_ready = wait_set.get_ready_entities('subscription') + guards_ready = wait_set.get_ready_entities('guard_condition') + + if guards_ready: + if sigint_gc.handle.pointer in guards_ready: + return (False, None) + + if subs_ready: + if sub.handle.pointer in subs_ready: + msg_info = sub.handle.take_message(sub.msg_type, sub.raw) + return (True, msg_info[0]) + + return (False, None) + + def save_info_csv(self): + print(f'\n writing to csv...') + saveQTable(args_parse.log_file_dir+'/Qtable.csv', self.Q_table) + np.savetxt(args_parse.log_file_dir+'/StateSpace.csv', self.state_space, '%d') + np.savetxt(args_parse.log_file_dir+'/steps_per_episode.csv', self.steps_per_episode, delimiter = ' , ') + np.savetxt(args_parse.log_file_dir+'/reward_per_episode.csv', self.reward_per_episode, delimiter = ' , ') + np.savetxt(args_parse.log_file_dir+'/T_per_episode.csv', self.T_per_episode, delimiter = ' , ') + np.savetxt(args_parse.log_file_dir+'/EPSILON_per_episode.csv', self.EPSILON_per_episode, delimiter = ' , ') + np.savetxt(args_parse.log_file_dir+'/reward_min_per_episode.csv', self.reward_min_per_episode, delimiter = ' , ') + np.savetxt(args_parse.log_file_dir+'/reward_max_per_episode.csv', self.reward_max_per_episode, delimiter = ' , ') + np.savetxt(args_parse.log_file_dir+'/reward_avg_per_episode.csv', self.reward_avg_per_episode, delimiter = ' , ') + np.savetxt(args_parse.log_file_dir+'/t_per_episode.csv', self.t_per_episode, delimiter = ' , ') + + def timer_callback(self): + _, msgScan = self.wait_for_message('/scan', LaserScan) + + # find time taken betwwen 2 callbacks + step_time = (self.get_clock().now() - self.t_step).nanoseconds / 1e9 + # self.prev_position = (999, 999) + + #if step_time > MIN_TIME_BETWEEN_ACTIONS: + self.t_step = self.get_clock().now() + if step_time > 2: + text = '\r\nTOO BIG STEP TIME: %.2f s' % step_time + print(text) + self.log_sim_info.write(text+'\r\n') + # Log data to file + self.save_info_csv() + + raise SystemExit + + # End of Learning + if self.episode > args_parse.max_episodes :#or self.terminal_state: + # simulation time + self.is_set_pos = False + # self.MAX_RADIUS = np.linalg.norm([X_INIT - GOAL_X, Y_INIT - GOAL_Y]) + # print(self.episode) + sim_time = (self.get_clock().now() - self.t_sim_start).nanoseconds / 1e9 + sim_time_h = sim_time // 3600 + sim_time_m = ( sim_time - sim_time_h * 3600 ) // 60 + sim_time_s = sim_time - sim_time_h * 3600 - sim_time_m * 60 + + # real time + # now_stop = datetime.now() + # # dt_string_stop = now_stop.strftime("%d/%m/%Y %H:%M:%S") + # real_time_delta = (now_stop - self.now_start).total_seconds() + # real_time_h = real_time_delta // 3600 + # real_time_m = ( real_time_delta - real_time_h * 3600 ) // 60 + # real_time_s = real_time_delta - real_time_h * 3600 - real_time_m * 60 + + # Log learning session info to file + text = '--------------------------------------- \r\n\r\n' + text = text + 'MAX EPISODES REACHED(%d), LEARNING FINISHED' % args_parse.max_episodes + '\r\n' + text = text + 'Simulation time: %d:%d:%d h/m/s \r\n' % (sim_time_h, sim_time_m, sim_time_s) + # text = text + 'Real time: %d:%d:%d h/m/s \r\n' % (real_time_h, real_time_m, real_time_s) + print(text) + self.log_sim_info.write('\r\n'+text+'\r\n') + self.log_sim_params.write(text+'\r\n') + + # Log data to file + self.save_info_csv() + + # Close files and shut down node + self.log_sim_info.close() + self.log_sim_params.close() + raise SystemExit + else: + ep_time = (self.get_clock().now() - self.t_ep).nanoseconds / 1e9 + # End of en Episode + print(f'\n episode {self.episode} of {args_parse.max_episodes}') + + if self.CUMULATIVE_REWARD < args_parse.reward_threshold or self.terminal_state: + robotStop(self.velPub) + print(f"\n End of episode. step: {self.ep_steps}") + print(f' CUMULATIVE_REWARD: {self.CUMULATIVE_REWARD}') + print(f' WIN_COUNT: {self.WIN_COUNT}') + # if self.crash: + # # get crash position + # _, odomMsg = self.wait_for_message('/odom', Odometry) + # ( x_crash , y_crash ) = getPosition(odomMsg) + # theta_crash = degrees(getRotation(odomMsg)) + + self.t_ep = self.get_clock().now() + self.reward_min = np.min(self.ep_reward_arr) + self.reward_max = np.max(self.ep_reward_arr) + self.reward_avg = np.mean(self.ep_reward_arr) + # now = datetime.now() + # dt_string = now.strftime("%d/%m/%Y %H:%M:%S") + text = '---------------------------------------\r\n' + # if self.terminal_state: + # text = text + '\r\nEpisode %d ==> CRASH {%.2f,%.2f,%.2f} ' % (self.episode, x_crash, y_crash, theta_crash) + dt_string + self.reset.call_async(self.dummy_req) + # elif self.ep_steps >= MAX_STEPS_PER_EPISODE: + # text = text + '\r\nEpisode %d ==> MAX STEPS PER EPISODE REACHED {%d} ' % (self.episode, MAX_STEPS_PER_EPISODE) + dt_string + # else: + # text = text + '\r\nEpisode %d ==> UNKNOWN TERMINAL CASE ' % self.episode + dt_string + text = text + '\r\nepisode time: %.2f s (avg step: %.2f s) \r\n' % (ep_time, ep_time / (self.ep_steps)) + text = text + 'episode steps: %d \r\n' % self.ep_steps + text = text + 'episode reward: %.2f \r\n' % self.ep_reward + text = text + 'episode max | avg | min reward: %.2f | %.2f | %.2f \r\n' % (self.reward_max, self.reward_avg, self.reward_min) + if args_parse.exploration_func == 1: + text = text + 'T = %f \r\n' % self.T + else: + text = text + 'EPSILON = %f \r\n' % self.EPSILON + print(text) + self.log_sim_info.write('\r\n'+text) + + self.steps_per_episode = np.append(self.steps_per_episode, self.ep_steps) + self.reward_per_episode = np.append(self.reward_per_episode, self.ep_reward) + self.T_per_episode = np.append(self.T_per_episode, self.T) + self.EPSILON_per_episode = np.append(self.EPSILON_per_episode, self.EPSILON) + self.t_per_episode = np.append(self.t_per_episode, ep_time) + self.reward_min_per_episode = np.append(self.reward_min_per_episode, self.reward_min) + self.reward_max_per_episode = np.append(self.reward_max_per_episode, self.reward_max) + self.reward_avg_per_episode = np.append(self.reward_avg_per_episode, self.reward_avg) + self.ep_reward_arr = np.array([]) + self.ep_steps = 0 + self.ep_reward = 0 + # cum reward reset + self.CUMULATIVE_REWARD = 0 + self.crash = 0 + self.robot_in_pos = False + self.first_action_taken = False + self.terminal_state = False + if self.T > T_MIN: + self.T = T_GRAD * self.T + if self.EPSILON > EPSILON_MIN: + self.EPSILON = EPSILON_GRAD * self.EPSILON + + # save to csv every n episodes + if self.episode % args_parse.max_episodes_before_save == 0: + print(f"saving data to csv every {args_parse.max_episodes_before_save} episodes") + if is_rand: + (self.GOAL_X, self.GOAL_Y) = np.random.choice(R,2) + GOAL_THETA = 0 + print((self.GOAL_X, self.GOAL_Y)) + else: + (self.GOAL_X, self.GOAL_Y, GOAL_THETA) = tuple(args_parse.GOAL_POSITION) + self.save_info_csv() + + + self.episode = self.episode + 1 + sleep(1.5) + else: + self.ep_steps = self.ep_steps + 1 + + # Initial position + if not self.is_set_pos: + _, odomMsg = self.wait_for_message('/odom', Odometry) + robotStop(self.velPub) + self.ep_steps = self.ep_steps - 1 + self.first_action_taken = False + # init pos + (x_set_init, y_set_init) = getPosition(odomMsg) + + if RANDOM_INIT_POS: + print('set random pos') + ( x_init , y_init , theta_init ) = robotSetRandomPos(self.setPosPub) + else: + print('set pos') + ( x_init , y_init , theta_init ) = robotSetPos(self.setPosPub, x_set_init, y_set_init, THETA_INIT) + + self.prev_position = getPosition(odomMsg) + self.MAX_RADIUS = np.linalg.norm([x_init - self.GOAL_X, y_init - self.GOAL_Y]) + # _, odomMsg = self.wait_for_message('/odom', Odometry) + # ( x , y ) = getPosition(odomMsg) + + # theta = degrees(getRotation(odomMsg)) + # check init pos + self.is_set_pos = True + # if abs(x-x_init) < 0.01 and abs(y-y_init) < 0.01 and abs(theta-theta_init) < 1: + # self.robot_in_pos = True + # #sleep(2) + # else: + # self.robot_in_pos = False + + # First acion + elif not self.first_action_taken: + _, odomMsg = self.wait_for_message('/odom', Odometry) #just added + ( current_x , current_y ) = getPosition(odomMsg) #just added + ( lidar, angles ) = lidarScan(msgScan) #just added + + + ( state_ind, x1, x2, x3 , x4 , x5, x6, x7, x8, x9, x10 ) = scanDiscretization(self.state_space, lidar, (self.GOAL_X, self.GOAL_Y), (current_x, current_y),self.prev_position, self.MAX_RADIUS, GOAL_RADIUS) + self.crash = checkCrash(lidar) + + if args_parse.exploration_func == 1 : + ( self.action, status_strat ) = softMaxSelection(self.Q_table, state_ind, self.actions, self.T) + else: + ( self.action, status_strat ) = epsiloGreedyExploration(self.Q_table, state_ind, self.actions, self.EPSILON) + + status_rda = robotDoAction(self.velPub, self.action) + + self.prev_lidar = lidar + self.prev_action = self.action + self.prev_state_ind = state_ind + + self.first_action_taken = True + + if not (status_strat == 'softMaxSelection => OK' or status_strat == 'epsiloGreedyExploration => OK'): + print('\r\n', status_strat, '\r\n') + self.log_sim_info.write('\r\n'+status_strat+'\r\n') + + if not status_rda == 'robotDoAction => OK': + print('\r\n', status_rda, '\r\n') + self.log_sim_info.write('\r\n'+status_rda+'\r\n') + + # Rest of the algorithm + else: + _, odomMsg = self.wait_for_message('/odom', Odometry) #just added + ( current_x , current_y ) = getPosition(odomMsg) #just added + ( lidar, angles ) = lidarScan(msgScan) + # print(self.prev_position,( current_x , current_y )) + + # get position + _, odomMsg = self.wait_for_message('/odom', Odometry) + yaw = getRotation(odomMsg) + + + ( state_ind, x1, x2, x3 , x4 , x5, x6, x7, x8, x9, x10 ) = scanDiscretization(self.state_space, lidar, (self.GOAL_X, self.GOAL_Y), (current_x, current_y),self.prev_position, self.MAX_RADIUS, GOAL_RADIUS) + self.crash = checkCrash(lidar) + + # radius caculated by norm of and goal position + + # ( reward, terminal_state ) = getReward(self.action, self.prev_action, lidar, self.prev_lidar, self.crash) + # getReward(action, prev_action,lidar, prev_lidar, crash, current_position, goal_position, max_radius, args_parse.radiaus_reduce_rate, nano_start_time, nano_current_time): + ( reward, self.terminal_state, win_count) = getReward(self.action, self.prev_action, lidar, self.prev_lidar, self.crash, + (current_x, current_y), + # self.prev_position, + (self.GOAL_X, self.GOAL_Y), + self.MAX_RADIUS, args_parse.radiaus_reduce_rate, ep_time , + self.get_clock().now().nanoseconds, + args_parse.GOAL_RADIUS, x10, self.WIN_COUNT) + print(f' CURRENT_REWARD: {reward}') + self.CUMULATIVE_REWARD += reward + self.WIN_COUNT = win_count + print(f' CUMULATIVE_REWARD: {self.CUMULATIVE_REWARD}') + print(f' WIN_COUNT: {self.WIN_COUNT}'+" "+f' GOAL: {(self.GOAL_X, self.GOAL_Y)}') + print(' CURRENT POSTION ({:.2f},{:.2f})'.format(current_x, current_y)) + + # print("time: ", self.get_clock().now().nanoseconds) + ( self.Q_table, status_uqt ) = updateQTable(self.Q_table, self.prev_state_ind, self.action, reward, state_ind, self.alpha, self.gamma) + + if args_parse.exploration_func == 1: + ( self.action, status_strat ) = softMaxSelection(self.Q_table, state_ind, self.actions, self.T) + else: + ( self.action, status_strat ) = epsiloGreedyExploration(self.Q_table, state_ind, self.actions, self.EPSILON) + + status_rda = robotDoAction(self.velPub, self.action) + + if not status_uqt == 'updateQTable => OK': + print('\r\n', status_uqt, '\r\n') + self.log_sim_info.write('\r\n'+status_uqt+'\r\n') + if not (status_strat == 'softMaxSelection => OK' or status_strat == 'epsiloGreedyExploration => OK'): + print('\r\n', status_strat, '\r\n') + self.log_sim_info.write('\r\n'+status_strat+'\r\n') + if not status_rda == 'robotDoAction => OK': + print('\r\n', status_rda, '\r\n') + self.log_sim_info.write('\r\n'+status_rda+'\r\n') + + self.ep_reward = self.ep_reward + reward + self.ep_reward_arr = np.append(self.ep_reward_arr, reward) + self.prev_lidar = lidar + self.prev_action = self.action + self.prev_state_ind = state_ind + + ( current_x , current_y ) = getPosition(odomMsg) + self.prev_position = (current_x, current_y) + +def main(ep_count, args=None): + rclpy.init(args=args) + # print('args_parse: ', args_parse) + movebase_publisher = LearningNode() + try: + rclpy.spin(movebase_publisher) + except SystemExit: # <--- process the exception + rclpy.logging.get_logger("End of learning").info('Done') + + + # Destroy the node explicitly + # (optional - otherwise it will be done automatically + movebase_publisher.destroy_node() + rclpy.shutdown() + + # rclpy.logging.get_logger("Time snap error").info(" at episodes: ", movebase_publisher.episode) + ep_count += movebase_publisher.episode + if ep_count < args_parse.max_episodes: + # main() + # print("I'm in :>") + main(ep_count= ep_count, args=None) + print("time skip error at episodes: ", movebase_publisher.episode, " total episodes: ", ep_count) + # print("End Idiot.") + +if __name__ == '__main__': + main(ep_count = 0, args = None) \ No newline at end of file