In [39]:
from pg_function_approx import *
In [40]:
%load_ext autoreload
%autoreload 2
%matplotlib inline
import numpy as np
The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
In [44]:
car1 = mountain_car(init_alpha= 0, type_features = 4, constant_alpha= True)
[2016-05-17 15:55:44,644] Making new env: MountainCar-v0
N_0 50.0
init alpha 0
Constant Alpha True
In [42]:
car1.train(iter=10, dataname ='' , save = False)
0 EPISODE #1.0
with a exploration of 100.0%
and learning rate of 0
lasted 7490 steps
-25.3276423541 -11.6423414862 -13.9685124897 -12.6084849363 -11.9553653235 -25.3276423541 -26.3525919093 theta
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
plotting the eligibility traces
plotting the value function
1 EPISODE #2.0
with a exploration of 98.0392156863%
and learning rate of 0
lasted 77576 steps
-66.1033796269 -70.7489289702 -79.2492549146 -93.4484442916 -90.2725207452 -83.6330455371 -92.0988388921 -85.2738697363 -93.457309238 -79.9229793722 -90.6175468107 -60.2382724877 -65.0521438591 -91.3158884059 -62.0457387925 -44.6780110928 -89.4457409374 -91.5274923079 -91.4221068156 -92.4890501308 -40.5025287725 -83.6753718083 -93.2273740509 -90.2022044376 -90.1089882836 -90.2678214071 -85.2227175644 -75.9917715432 -79.9229793722 -47.2781189611 -46.2497383748 -62.0032374303 -71.7511950352 -92.5406062435 -84.963110376 -91.3295962707 -82.4491886993 -87.4212881212 -93.1339005959 -90.4348918281 -93.2273740509 -88.4609821363 -92.5406062435 -92.4252654175 -91.1983649842 -84.5847666374 -76.8692021345 -79.2492549146 -62.0032374303 -81.6141611884 -89.1948605011 -93.5038581861 -93.3439645047 -86.2348172151 -92.0536051956 -90.8437141399 -93.1160522685 -84.5847666374 -84.373997494 -79.6040330329 -92.0988388921 -51.8429405474 -59.7751977983 -56.9431249485 -26.370272982 -91.5274923079 -91.5274923079 -93.2384397678 -67.1475074172 -93.1339005959 -91.1845592906 -93.2229331258 -92.18489977 -93.1339005959 -83.6753718083 -82.9477921166 -92.2166063061 theta
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
plotting the eligibility traces
plotting the value function
2 EPISODE #3.0
with a exploration of 96.1538461538%
and learning rate of 0
lasted 0 steps
theta
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
plotting the eligibility traces
plotting the value function
3 EPISODE #4.0
with a exploration of 94.3396226415%
and learning rate of 0
lasted 59933 steps
-99.3574905073 -96.4373925441 -47.7298049941 -60.6954007173 -97.0429769081 -97.5327556268 -97.1228188546 -94.4304559354 -98.8308052183 -98.6629231212 -75.4450519554 -97.3747501334 -92.4533885 -38.9084341383 -97.1228188546 -96.0197473202 -84.4461621589 -98.6822876155 -98.8012924838 -94.365316702 -91.6486786217 -72.5559880499 -85.3599329092 -78.3813865201 -98.75902822 -98.6855447 -98.9498730529 -99.163840145 -98.4731053005 -98.3929373473 -97.3075912602 -98.7571192052 -99.1312251318 -75.4450519554 -83.790468438 -97.1228188546 -91.6486786217 -65.4591028095 -97.0680726291 -84.489483794 -98.9494516303 -98.7571192052 -98.5911461229 -97.8998131102 -98.8301011362 -99.0962732158 -96.357342282 -91.6486786217 -96.7781106647 -98.3812918245 -96.7011278857 -98.4418331028 -81.51761058 -98.3812918245 -98.7284670056 -98.8261927555 -98.7571192052 -99.1312251318 -98.4870470141 theta
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
plotting the eligibility traces
plotting the value function
4 EPISODE #5.0
with a exploration of 92.5925925926%
and learning rate of 0
lasted 88191 steps
-97.4412422623 -96.0085525264 -100.714380577 -100.512491816 -98.807584868 -102.896080317 -99.9337529665 -100.549488008 -98.3154542879 -99.9357340391 -98.2392802038 -102.277868202 -99.9894064994 -100.432685254 -99.5806990029 -103.025803544 -89.6212187388 -97.7934401089 -85.6808741805 -100.412073892 -102.271848551 -99.9641911934 -102.271848551 -100.396640623 -98.3165938515 -99.3057559803 -88.4714279641 -99.6202567857 -97.9933518367 -99.7248479194 -99.6850796044 -100.164682278 -98.3154542879 -99.2187424874 -99.9028039741 -99.898737661 -99.4400797693 -95.2912231166 -97.5001972429 -97.4412422623 -99.9097712796 -99.7615448193 -99.9807618557 -99.5909236779 -90.6198454885 -101.962587188 -98.2451991067 -99.8218597598 -99.9357340391 -102.277868202 -99.4852398418 -88.6180459591 -93.4071325863 -101.962587188 -100.026268533 -99.9337529665 -99.898737661 -99.9754524274 -99.9807618557 -101.962587188 -101.200212239 -90.6198454885 -105.209768787 -72.3106258064 -76.8371615306 -97.9933518367 -99.9516969095 -99.9568308696 -100.449490897 -100.484697357 -97.7716305238 -97.8553006533 -97.5001972429 -73.6833026024 -99.8019798206 -99.7248479194 -99.9028039741 -100.412073892 -99.7853517684 -68.8014557949 -59.4342677935 -97.9933518367 -102.896080317 -79.7327547068 -96.7977155312 -100.109199993 -99.9568308696 -98.7117981557 theta
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
plotting the eligibility traces
plotting the value function
5 EPISODE #6.0
with a exploration of 90.9090909091%
and learning rate of 0
lasted 10557 steps
-99.2509172228 -99.5714409361 -98.9666530228 -99.4851203285 -99.1531613341 -54.7038230419 -78.1508297706 -98.7502809167 -98.9419995091 -99.8414235665 theta
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
plotting the eligibility traces
plotting the value function
6 EPISODE #7.0
with a exploration of 89.2857142857%
and learning rate of 0
lasted 54870 steps
-99.7180108197 -99.5891863641 -65.602736862 -98.3599459938 -99.513453345 -100.232688154 -100.897601998 -103.701520292 -75.7402434808 -100.232688154 -97.2046706714 -97.585618169 -99.9249617048 -101.710913392 -91.3649709912 -96.7827012669 -98.8422029562 -98.74165145 -99.958751293 -89.3369636642 -94.5824398913 -101.069953083 -100.060004426 -100.202804494 -98.9570240694 -99.1500999682 -99.9677874489 -99.8455051996 -101.198305294 -89.9400769369 -100.162662393 -99.5898994706 -105.810403338 -100.82396932 -99.7056703679 -99.8679820611 -99.9341958426 -99.9330180926 -98.8558716941 -99.7603011072 -99.4987194829 -92.257139982 -97.9835184005 -101.935455771 -100.187139671 -100.584667898 -96.4103010552 -99.5891863641 -99.9053704772 -100.755168699 -99.8125396208 -99.2549445267 -89.3369636642 -100.763950465 theta
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
plotting the eligibility traces
plotting the value function
7 EPISODE #8.0
with a exploration of 87.7192982456%
and learning rate of 0
lasted 4454 steps
-103.272959216 -100.20054718 -99.546669494 -100.675782896 theta
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
plotting the eligibility traces
plotting the value function
8 EPISODE #9.0
with a exploration of 86.2068965517%
and learning rate of 0
lasted 5098 steps
-97.847236617 -102.321907952 -99.2396376164 -100.21013352 -99.9353451054 theta
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
plotting the eligibility traces
plotting the value function
9 EPISODE #10.0
with a exploration of 84.7457627119%
and learning rate of 0
lasted 38479 steps
-103.325502339 -100.419163524 -100.178618093 -99.8272104917 -100.865056108 -99.4019455718 -99.4294309878 -102.311440555 -78.050611178 -97.178206133 -96.8759419534 -85.6807470402 -99.8245045519 -89.2020634951 -101.198465883 -99.4169285386 -98.6646896715 -100.0450675 -100.056447662 -100.823629312 -103.336222602 -100.865056108 -103.840703147 -85.6625577047 -101.198465883 -98.3770559802 -64.0645277002 -98.3487014299 -100.908602059 -99.8272104917 -99.5582135218 -99.9616082545 -100.49409031 -82.8799981922 -99.4294309878 -99.9327562464 -100.002807737 -100.050047964 theta
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
plotting the eligibility traces
plotting the value function
Out[42]:
array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
In [43]:
car1.init_alpha = 1e-3
car1.alpha = car1.init_alpha
car1.epsilon = 1.0
car1.total_runs = 0.
car1.constant_alpha = False
car1.train(iter=1000, dataname ='' , save = False)
0 EPISODE #1.0
with a exploration of 100.0%
and learning rate of 0
lasted 85066 steps
-47.593740713 -100.714823758 -93.3937051507 -103.089774803 -99.7381310749 -101.026557783 -100.079389394 -54.5487386752 -100.37240922 -101.026557783 -100.154871126 -99.8309880622 -98.4496269103 -99.987313781 -100.002076396 -100.260572856 -94.9267260452 -98.9559215198 -58.28963941 -102.109725913 -99.897554072 -99.9868673799 -101.984446027 -102.620894872 -99.8449177151 -100.078811709 -99.6298247221 -99.9265601316 -98.8835670872 -98.9574890302 -100.217525484 -99.8309880622 -100.260572856 -101.026557783 -98.5659462068 -94.9267260452 -99.2695838932 -99.2695838932 -97.7342767978 -99.4413216481 -99.6945549708 -100.145769403 -100.014551596 -99.8086714903 -100.029022955 -98.2381591161 -96.2641303517 -98.2381591161 -98.5659462068 -99.2608287809 -99.0724010566 -86.2463298339 -100.154871126 -100.029022955 -99.9172469652 -99.8883723217 -94.9267260452 -101.865642365 -99.8883723217 -99.3149629464 -99.5499800481 -99.9868673799 -99.9781039852 -100.137811804 -99.7868672898 -99.9265601316 -102.079564901 -100.129296035 -99.9172469652 -95.3289405379 -98.2096341696 -100.961983899 -97.4935238323 -99.9411363545 -84.6003324114 -100.568680082 -101.865642365 -100.179077048 -107.143994057 -99.4691297728 -100.714823758 -98.7473558836 -101.406129221 -107.143994057 -99.5499800481 theta
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
plotting the eligibility traces
plotting the value function
1 EPISODE #2.0
with a exploration of 98.0392156863%
and learning rate of 0.001
lasted 2383 steps
-98.9063759433 -100.033901853 theta
[-0.14115179 -0.06677876 0.10780957 -0.03475121 0.11245785 -0.00759001
-0.06168792 -0.0045866 0.02869394 0.07436877 -0.04612165 0.03933781]
plotting the eligibility traces
plotting the value function
2 EPISODE #3.0
with a exploration of 96.1538461538%
and learning rate of 0.000707106781187
lasted 34619 steps
-99.4028851385 -99.4762995078 -99.5896405122 -97.3572688958 -99.2922793654 -101.556428077 -101.000376413 -88.5524456701 -95.4204429106 -99.9811327037 -101.60424577 -100.230950534 -101.373361746 -99.9971594171 -99.8964603575 -100.031761848 -102.014051103 -101.556428077 -99.42258084 -99.1637301891 -100.111701116 -98.966332415 -103.734225814 -99.1637301891 -98.4926430264 -101.152878339 -99.1637301891 -99.1637301891 -100.849468041 -100.201713301 -100.13757126 -95.2196652825 -88.0607166973 -99.9971594171 theta
[-0.24982234 0.43428164 0.23318239 0.00746669 -0.11202685 0.29294462
-0.0612671 0.02035891 0.3618492 -0.72722626 -0.17191528 -0.0278256 ]
plotting the eligibility traces
plotting the value function
3 EPISODE #4.0
with a exploration of 94.3396226415%
and learning rate of 0.00057735026919
lasted 35224 steps
-96.0194000898 -100.377969565 -99.9764684303 -99.5266427974 -98.0662369791 -101.30675184 -99.5666429129 -98.1805068564 -100.985777991 -100.111119226 -101.72930583 -101.406428776 -100.228572262 -101.117353533 -102.359914239 -101.117353533 -99.7127198347 -100.293498313 -99.2295568737 -99.8366993637 -100.325064861 -89.057798032 -101.015947209 -100.970499412 -91.6519315296 -98.3365727891 -101.478079691 -99.7163529759 -97.1336010182 -99.5385346974 -99.5288475573 -99.8366243827 -99.6508258154 -99.7808028108 -99.9289410002 theta
[-0.48776437 0.93200285 0.3441129 0.01317685 -0.21534304 0.42461211
-0.03009344 0.04830801 0.70310742 -1.35661496 -0.31401946 -0.06148487]
plotting the eligibility traces
plotting the value function
4 EPISODE #5.0
with a exploration of 92.5925925926%
and learning rate of 0.0005
lasted 0 steps
theta
[-0.48776437 0.93200285 0.3441129 0.01317685 -0.21534304 0.42461211
-0.03009344 0.04830801 0.70310742 -1.35661496 -0.31401946 -0.06148487]
plotting the eligibility traces
plotting the value function
5 EPISODE #6.0
with a exploration of 90.9090909091%
and learning rate of 0.0004472135955
lasted 8867 steps
-101.680352637 -101.992083545 -103.877490397 -99.8189689513 -99.4333474471 -99.4646494485 -100.031712141 -99.9483767972 theta
[-0.45766843 0.94809768 0.32445914 0.01210086 -0.24513818 0.43636173
-0.01783117 0.0571517 0.70280661 -1.38445941 -0.30662797 -0.06925256]
plotting the eligibility traces
plotting the value function
6 EPISODE #7.0
with a exploration of 89.2857142857%
and learning rate of 0.000408248290464
lasted 83192 steps
-99.929070862 -100.190797986 -100.113119059 -100.492137028 -99.9474262598 -100.05557522 -99.3930348913 -102.669209953 -100.963800785 -99.8725304285 -99.3591331372 -100.12014399 -99.8611713826 -100.12014399 -100.413899629 -100.492137028 -100.113119059 -99.728685548 -99.8725304285 -99.7732678048 -99.9474262598 -100.094432673 -100.01385332 -101.030409993 -98.315689041 -99.9611419108 -99.9611419108 -98.4267368007 -99.6719975934 -98.8159008538 -99.8229532849 -99.3930348913 -99.8319282932 -99.6217268427 -100.497918549 -101.203985979 -101.010475127 -101.424017128 -100.059127277 -99.3556908706 -100.989559756 -100.046626358 -99.8045929954 -98.8159008538 -100.133028848 -99.7732678048 -99.8725304285 -99.8527011789 -100.287933173 -99.6067252052 -99.9474262598 -100.287933173 -99.2873215392 -99.9906537202 -102.331941308 -100.193976088 -99.8045929954 -99.8638819616 -99.8329586866 -99.8725304285 -99.9828626556 -99.6858969841 -99.9828626556 -98.4267368007 -99.6217268427 -99.7886901429 -100.565904677 -100.972462032 -98.4798884166 -99.9828626556 -99.8725304285 -99.9770092188 -100.049217298 -99.8638819616 -99.9449737146 -99.9770092188 -100.963800785 -100.065815333 -100.193976088 -99.8611713826 -99.3410181205 -99.9062119035 -99.9474262598 theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
7 EPISODE #8.0
with a exploration of 87.7192982456%
and learning rate of 0.000377964473009
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
8 EPISODE #9.0
with a exploration of 86.2068965517%
and learning rate of 0.000353553390593
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
9 EPISODE #10.0
with a exploration of 84.7457627119%
and learning rate of 0.000333333333333
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
10 EPISODE #11.0
with a exploration of 83.3333333333%
and learning rate of 0.000316227766017
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
11 EPISODE #12.0
with a exploration of 81.9672131148%
and learning rate of 0.000301511344578
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
12 EPISODE #13.0
with a exploration of 80.6451612903%
and learning rate of 0.000288675134595
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
13 EPISODE #14.0
with a exploration of 79.3650793651%
and learning rate of 0.000277350098113
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
14 EPISODE #15.0
with a exploration of 78.125%
and learning rate of 0.000267261241912
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
15 EPISODE #16.0
with a exploration of 76.9230769231%
and learning rate of 0.000258198889747
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
16 EPISODE #17.0
with a exploration of 75.7575757576%
and learning rate of 0.00025
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
17 EPISODE #18.0
with a exploration of 74.6268656716%
and learning rate of 0.000242535625036
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
18 EPISODE #19.0
with a exploration of 73.5294117647%
and learning rate of 0.000235702260396
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
19 EPISODE #20.0
with a exploration of 72.4637681159%
and learning rate of 0.000229415733871
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
20 EPISODE #21.0
with a exploration of 71.4285714286%
and learning rate of 0.00022360679775
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
21 EPISODE #22.0
with a exploration of 70.4225352113%
and learning rate of 0.000218217890236
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
22 EPISODE #23.0
with a exploration of 69.4444444444%
and learning rate of 0.000213200716356
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
23 EPISODE #24.0
with a exploration of 68.4931506849%
and learning rate of 0.000208514414057
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
24 EPISODE #25.0
with a exploration of 67.5675675676%
and learning rate of 0.000204124145232
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
25 EPISODE #26.0
with a exploration of 66.6666666667%
and learning rate of 0.0002
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
26 EPISODE #27.0
with a exploration of 65.7894736842%
and learning rate of 0.000196116135138
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
27 EPISODE #28.0
with a exploration of 64.9350649351%
and learning rate of 0.00019245008973
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
28 EPISODE #29.0
with a exploration of 64.1025641026%
and learning rate of 0.000188982236505
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
29 EPISODE #30.0
with a exploration of 63.2911392405%
and learning rate of 0.000185695338177
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
30 EPISODE #31.0
with a exploration of 62.5%
and learning rate of 0.000182574185835
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
31 EPISODE #32.0
with a exploration of 61.7283950617%
and learning rate of 0.000179605302027
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
32 EPISODE #33.0
with a exploration of 60.9756097561%
and learning rate of 0.000176776695297
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
33 EPISODE #34.0
with a exploration of 60.2409638554%
and learning rate of 0.000174077655956
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
34 EPISODE #35.0
with a exploration of 59.5238095238%
and learning rate of 0.000171498585143
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
35 EPISODE #36.0
with a exploration of 58.8235294118%
and learning rate of 0.000169030850946
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
36 EPISODE #37.0
with a exploration of 58.1395348837%
and learning rate of 0.000166666666667
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
37 EPISODE #38.0
with a exploration of 57.4712643678%
and learning rate of 0.000164398987305
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
38 EPISODE #39.0
with a exploration of 56.8181818182%
and learning rate of 0.000162221421131
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
39 EPISODE #40.0
with a exploration of 56.1797752809%
and learning rate of 0.000160128153805
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
40 EPISODE #41.0
with a exploration of 55.5555555556%
and learning rate of 0.000158113883008
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
41 EPISODE #42.0
with a exploration of 54.9450549451%
and learning rate of 0.000156173761889
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
42 EPISODE #43.0
with a exploration of 54.347826087%
and learning rate of 0.000154303349962
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
43 EPISODE #44.0
with a exploration of 53.7634408602%
and learning rate of 0.000152498570333
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
44 EPISODE #45.0
with a exploration of 53.1914893617%
and learning rate of 0.000150755672289
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
45 EPISODE #46.0
with a exploration of 52.6315789474%
and learning rate of 0.0001490711985
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
46 EPISODE #47.0
with a exploration of 52.0833333333%
and learning rate of 0.000147441956155
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
47 EPISODE #48.0
with a exploration of 51.5463917526%
and learning rate of 0.000145864991498
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
48 EPISODE #49.0
with a exploration of 51.0204081633%
and learning rate of 0.000144337567297
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
49 EPISODE #50.0
with a exploration of 50.5050505051%
and learning rate of 0.000142857142857
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
50 EPISODE #51.0
with a exploration of 50.0%
and learning rate of 0.000141421356237
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
51 EPISODE #52.0
with a exploration of 49.504950495%
and learning rate of 0.000140028008403
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
52 EPISODE #53.0
with a exploration of 49.0196078431%
and learning rate of 0.000138675049056
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
53 EPISODE #54.0
with a exploration of 48.5436893204%
and learning rate of 0.000137360563949
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
54 EPISODE #55.0
with a exploration of 48.0769230769%
and learning rate of 0.000136082763488
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
55 EPISODE #56.0
with a exploration of 47.619047619%
and learning rate of 0.000134839972493
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
56 EPISODE #57.0
with a exploration of 47.1698113208%
and learning rate of 0.000133630620956
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
57 EPISODE #58.0
with a exploration of 46.7289719626%
and learning rate of 0.000132453235707
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
58 EPISODE #59.0
with a exploration of 46.2962962963%
and learning rate of 0.00013130643286
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
59 EPISODE #60.0
with a exploration of 45.871559633%
and learning rate of 0.000130188910981
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
60 EPISODE #61.0
with a exploration of 45.4545454545%
and learning rate of 0.000129099444874
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
61 EPISODE #62.0
with a exploration of 45.045045045%
and learning rate of 0.000128036879933
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
62 EPISODE #63.0
with a exploration of 44.6428571429%
and learning rate of 0.000127000127
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
63 EPISODE #64.0
with a exploration of 44.2477876106%
and learning rate of 0.00012598815767
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
64 EPISODE #65.0
with a exploration of 43.8596491228%
and learning rate of 0.000125
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
65 EPISODE #66.0
with a exploration of 43.4782608696%
and learning rate of 0.000124034734589
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
66 EPISODE #67.0
with a exploration of 43.1034482759%
and learning rate of 0.000123091490979
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
67 EPISODE #68.0
with a exploration of 42.735042735%
and learning rate of 0.000122169444356
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
68 EPISODE #69.0
with a exploration of 42.3728813559%
and learning rate of 0.000121267812518
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
69 EPISODE #70.0
with a exploration of 42.0168067227%
and learning rate of 0.000120385853086
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
70 EPISODE #71.0
with a exploration of 41.6666666667%
and learning rate of 0.000119522860933
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
71 EPISODE #72.0
with a exploration of 41.3223140496%
and learning rate of 0.000118678165819
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
72 EPISODE #73.0
with a exploration of 40.9836065574%
and learning rate of 0.000117851130198
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
73 EPISODE #74.0
with a exploration of 40.6504065041%
and learning rate of 0.000117041147196
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
74 EPISODE #75.0
with a exploration of 40.3225806452%
and learning rate of 0.000116247638744
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
75 EPISODE #76.0
with a exploration of 40.0%
and learning rate of 0.000115470053838
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
76 EPISODE #77.0
with a exploration of 39.6825396825%
and learning rate of 0.000114707866935
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
77 EPISODE #78.0
with a exploration of 39.3700787402%
and learning rate of 0.00011396057646
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
78 EPISODE #79.0
with a exploration of 39.0625%
and learning rate of 0.000113227703414
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
79 EPISODE #80.0
with a exploration of 38.7596899225%
and learning rate of 0.000112508790093
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
80 EPISODE #81.0
with a exploration of 38.4615384615%
and learning rate of 0.000111803398875
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
81 EPISODE #82.0
with a exploration of 38.1679389313%
and learning rate of 0.000111111111111
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
82 EPISODE #83.0
with a exploration of 37.8787878788%
and learning rate of 0.000110431526075
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
83 EPISODE #84.0
with a exploration of 37.5939849624%
and learning rate of 0.00010976425999
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
84 EPISODE #85.0
with a exploration of 37.3134328358%
and learning rate of 0.000109108945118
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
85 EPISODE #86.0
with a exploration of 37.037037037%
and learning rate of 0.000108465228909
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
86 EPISODE #87.0
with a exploration of 36.7647058824%
and learning rate of 0.000107832773203
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
87 EPISODE #88.0
with a exploration of 36.496350365%
and learning rate of 0.000107211253484
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
88 EPISODE #89.0
with a exploration of 36.231884058%
and learning rate of 0.000106600358178
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
89 EPISODE #90.0
with a exploration of 35.9712230216%
and learning rate of 0.000105999788001
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
90 EPISODE #91.0
with a exploration of 35.7142857143%
and learning rate of 0.000105409255339
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
91 EPISODE #92.0
with a exploration of 35.4609929078%
and learning rate of 0.000104828483672
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
92 EPISODE #93.0
with a exploration of 35.2112676056%
and learning rate of 0.000104257207029
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
93 EPISODE #94.0
with a exploration of 34.965034965%
and learning rate of 0.000103695169473
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
94 EPISODE #95.0
with a exploration of 34.7222222222%
and learning rate of 0.000103142124626
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
95 EPISODE #96.0
with a exploration of 34.4827586207%
and learning rate of 0.000102597835209
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
96 EPISODE #97.0
with a exploration of 34.2465753425%
and learning rate of 0.000102062072616
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
97 EPISODE #98.0
with a exploration of 34.0136054422%
and learning rate of 0.000101534616513
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
98 EPISODE #99.0
with a exploration of 33.7837837838%
and learning rate of 0.000101015254455
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
99 EPISODE #100.0
with a exploration of 33.5570469799%
and learning rate of 0.000100503781526
lasted 0 steps
theta
[-0.53285532 1.03530655 0.37479792 0.00843078 -0.20222704 0.48423867
-0.02654878 0.03981044 0.73508236 -1.51954522 -0.34824915 -0.04824122]
plotting the eligibility traces
plotting the value function
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-43-462ee0a0bae1> in <module>()
3 car1.total_runs = 0.
4 car1.constant_alpha = False
----> 5 car1.train(iter=1000, dataname ='' , save = False)
/home/frederik/Dokumente/DeepRL/PolicyGradient/pg_function_approx.py in train(self, iter, dataname, save)
366 previous_state = state
367
--> 368 # theta += alpha*calculate_gradient()
369 # Version reversed (equivalent if only updated at the end)
370 theta_update = np.zeros_like(self.theta)
/home/frederik/Dokumente/DeepRL/PolicyGradient/pg_function_approx.py in plot_testing(self)
510 v_range = np.linspace(obs_low[1], obs_high[1], resolution)
511
--> 512 # get actions in a grid
513 greedy_policy = np.zeros((resolution, resolution))
514 for i, x in enumerate(x_range):
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/pyplot.pyc in yscale(*args, **kwargs)
1639 %(scale_docs)s
1640 """
-> 1641 gca().set_yscale(*args, **kwargs)
1642
1643
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_base.pyc in set_yscale(self, value, **kwargs)
3094 ax._update_transScale()
3095 ax.stale = True
-> 3096 self.autoscale_view(scalex=False)
3097
3098 def get_yticks(self, minor=False):
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_base.pyc in autoscale_view(self, tight, scalex, scaley)
2195 y1 += delta
2196 if not _tight:
-> 2197 y0, y1 = ylocator.view_limits(y0, y1)
2198 self.set_ybound(y0, y1)
2199
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/ticker.pyc in view_limits(self, vmin, vmax)
1611 if minpos <= 0 or not np.isfinite(minpos):
1612 raise ValueError(
-> 1613 "Data has no positive values, and therefore can not be "
1614 "log-scaled.")
1615
ValueError: Data has no positive values, and therefore can not be log-scaled.
Error in callback <function post_execute at 0x7fe5ceb01500> (for post_execute):
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/pyplot.pyc in post_execute()
145 def post_execute():
146 if matplotlib.is_interactive():
--> 147 draw_all()
148
149 # IPython >= 2
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/_pylab_helpers.pyc in draw_all(cls, force)
148 for f_mgr in cls.get_all_fig_managers():
149 if force or f_mgr.canvas.figure.stale:
--> 150 f_mgr.canvas.draw_idle()
151
152 atexit.register(Gcf.destroy_all)
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/backend_bases.pyc in draw_idle(self, *args, **kwargs)
2024 if not self._is_idle_drawing:
2025 with self._idle_draw_cntx():
-> 2026 self.draw(*args, **kwargs)
2027
2028 def draw_cursor(self, event):
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc in draw(self)
472
473 try:
--> 474 self.figure.draw(self.renderer)
475 finally:
476 RendererAgg.lock.release()
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/artist.pyc in draw_wrapper(artist, renderer, *args, **kwargs)
59 def draw_wrapper(artist, renderer, *args, **kwargs):
60 before(artist, renderer)
---> 61 draw(artist, renderer, *args, **kwargs)
62 after(artist, renderer)
63
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/figure.pyc in draw(self, renderer)
1157 dsu.sort(key=itemgetter(0))
1158 for zorder, a, func, args in dsu:
-> 1159 func(*args)
1160
1161 renderer.close_group('figure')
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/artist.pyc in draw_wrapper(artist, renderer, *args, **kwargs)
59 def draw_wrapper(artist, renderer, *args, **kwargs):
60 before(artist, renderer)
---> 61 draw(artist, renderer, *args, **kwargs)
62 after(artist, renderer)
63
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_base.pyc in draw(self, renderer, inframe)
2322
2323 for zorder, a in dsu:
-> 2324 a.draw(renderer)
2325
2326 renderer.close_group('axes')
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/artist.pyc in draw_wrapper(artist, renderer, *args, **kwargs)
59 def draw_wrapper(artist, renderer, *args, **kwargs):
60 before(artist, renderer)
---> 61 draw(artist, renderer, *args, **kwargs)
62 after(artist, renderer)
63
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/axis.pyc in draw(self, renderer, *args, **kwargs)
1104 renderer.open_group(__name__)
1105
-> 1106 ticks_to_draw = self._update_ticks(renderer)
1107 ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,
1108 renderer)
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/axis.pyc in _update_ticks(self, renderer)
947
948 interval = self.get_view_interval()
--> 949 tick_tups = [t for t in self.iter_ticks()]
950 if self._smart_bounds:
951 # handle inverted limits
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/axis.pyc in iter_ticks(self)
890 Iterate through all of the major and minor ticks.
891 """
--> 892 majorLocs = self.major.locator()
893 majorTicks = self.get_major_ticks(len(majorLocs))
894 self.major.formatter.set_locs(majorLocs)
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/ticker.pyc in __call__(self)
1536 'Return the locations of the ticks'
1537 vmin, vmax = self.axis.get_view_interval()
-> 1538 return self.tick_values(vmin, vmax)
1539
1540 def tick_values(self, vmin, vmax):
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/ticker.pyc in tick_values(self, vmin, vmax)
1554 if vmin <= 0.0 or not np.isfinite(vmin):
1555 raise ValueError(
-> 1556 "Data has no positive values, and therefore can not be "
1557 "log-scaled.")
1558
ValueError: Data has no positive values, and therefore can not be log-scaled.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/frederik/anaconda2/lib/python2.7/site-packages/IPython/core/formatters.pyc in __call__(self, obj)
337 pass
338 else:
--> 339 return printer(obj)
340 # Finally look for special method names
341 method = _safe_get_formatter_method(obj, self.print_method)
/home/frederik/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.pyc in <lambda>(fig)
226
227 if 'png' in formats:
--> 228 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
229 if 'retina' in formats or 'png2x' in formats:
230 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs))
/home/frederik/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.pyc in print_figure(fig, fmt, bbox_inches, **kwargs)
117
118 bytes_io = BytesIO()
--> 119 fig.canvas.print_figure(bytes_io, **kw)
120 data = bytes_io.getvalue()
121 if fmt == 'svg':
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/backend_bases.pyc in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)
2178 orientation=orientation,
2179 dryrun=True,
-> 2180 **kwargs)
2181 renderer = self.figure._cachedRenderer
2182 bbox_inches = self.figure.get_tightbbox(renderer)
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc in print_png(self, filename_or_obj, *args, **kwargs)
525
526 def print_png(self, filename_or_obj, *args, **kwargs):
--> 527 FigureCanvasAgg.draw(self)
528 renderer = self.get_renderer()
529 original_dpi = renderer.dpi
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc in draw(self)
472
473 try:
--> 474 self.figure.draw(self.renderer)
475 finally:
476 RendererAgg.lock.release()
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/artist.pyc in draw_wrapper(artist, renderer, *args, **kwargs)
59 def draw_wrapper(artist, renderer, *args, **kwargs):
60 before(artist, renderer)
---> 61 draw(artist, renderer, *args, **kwargs)
62 after(artist, renderer)
63
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/figure.pyc in draw(self, renderer)
1157 dsu.sort(key=itemgetter(0))
1158 for zorder, a, func, args in dsu:
-> 1159 func(*args)
1160
1161 renderer.close_group('figure')
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/artist.pyc in draw_wrapper(artist, renderer, *args, **kwargs)
59 def draw_wrapper(artist, renderer, *args, **kwargs):
60 before(artist, renderer)
---> 61 draw(artist, renderer, *args, **kwargs)
62 after(artist, renderer)
63
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_base.pyc in draw(self, renderer, inframe)
2322
2323 for zorder, a in dsu:
-> 2324 a.draw(renderer)
2325
2326 renderer.close_group('axes')
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/artist.pyc in draw_wrapper(artist, renderer, *args, **kwargs)
59 def draw_wrapper(artist, renderer, *args, **kwargs):
60 before(artist, renderer)
---> 61 draw(artist, renderer, *args, **kwargs)
62 after(artist, renderer)
63
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/axis.pyc in draw(self, renderer, *args, **kwargs)
1104 renderer.open_group(__name__)
1105
-> 1106 ticks_to_draw = self._update_ticks(renderer)
1107 ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,
1108 renderer)
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/axis.pyc in _update_ticks(self, renderer)
947
948 interval = self.get_view_interval()
--> 949 tick_tups = [t for t in self.iter_ticks()]
950 if self._smart_bounds:
951 # handle inverted limits
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/axis.pyc in iter_ticks(self)
890 Iterate through all of the major and minor ticks.
891 """
--> 892 majorLocs = self.major.locator()
893 majorTicks = self.get_major_ticks(len(majorLocs))
894 self.major.formatter.set_locs(majorLocs)
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/ticker.pyc in __call__(self)
1536 'Return the locations of the ticks'
1537 vmin, vmax = self.axis.get_view_interval()
-> 1538 return self.tick_values(vmin, vmax)
1539
1540 def tick_values(self, vmin, vmax):
/home/frederik/anaconda2/lib/python2.7/site-packages/matplotlib/ticker.pyc in tick_values(self, vmin, vmax)
1554 if vmin <= 0.0 or not np.isfinite(vmin):
1555 raise ValueError(
-> 1556 "Data has no positive values, and therefore can not be "
1557 "log-scaled.")
1558
ValueError: Data has no positive values, and therefore can not be log-scaled.
<matplotlib.figure.Figure at 0x7fe5ad721c10>
In [7]:
%matplotlib
car1.plot_value_function()
Using matplotlib backend: Qt4Agg
plotting the value function
In [4]:
car1.loaddata(dataname ='alpha_1e-3_feature4_schedule_50_100000iter')
episodelenghts_feature4 = car1.episode_lengths
testlenghts_feature4 = car1.test_lengths
[[-2.12793301 0.26071573 1.86721728]
[-3.34394784 -2.43359782 5.77754566]
[ 1.05304602 0.14679821 -1.19984423]
[-1.23776442 -0.94353312 2.18129755]]
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In [6]:
import matplotlib.pyplot as plt
%matplotlib
fig = plt.figure()
plt.plot(episodelenghts_feature4,label="quadratic features")
plt.yscale('log')
plt.legend( loc=1)
plt.xlabel("episodes")
plt.ylabel("Episode Length ")
plt.show()
Using matplotlib backend: Qt4Agg
In [13]:
fig = plt.figure()
plt.plot(testlenghts_feature4,label="deterministic policy")
plt.yscale('log')
plt.legend( loc=1)
plt.xlabel("episodes")
plt.ylabel("Episode Length ")
plt.show()
In [9]:
car1.plot_policy(mode ='deterministic')
In [ ]:
Content source: febert/DeepRL
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