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journal.txt
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Tue 01 May 2018 02:07:41 PM PDT
Dang - not even a billion will work, haha
N = 1000000000 = 1000000000
Progress: 100%|█████████████████████████████████████████| Time: 1:16:15
tree.v[best_node] = -4.788946905946339
Should be -5.33
Tue 01 May 2018 08:52:06 AM PDT
zsunberg@bethpage:~/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments$ julia -p 54 multilane/compare.jl
cor = 0.0 = 0.0
lambda = 2.0 = 2.0
N = 1000 = 1000
n_iters = 1000000 = 1000000
max_time = 1.0 = 1.0
max_depth = 40 = 40
val = SimpleSolver() = Multilane.SimpleSolver()
k = "qmdp"
Simulating...100%|██████████████████████████████████████| Time: 0:11:54
reward: 0.296 ± 0.009
k = "pftdpw_5"
Simulating...100%|██████████████████████████████████████| Time: 0:14:18
reward: 0.207 ± 0.008
k = "pftdpw_10"
Simulating...100%|██████████████████████████████████████| Time: 0:14:32
reward: 0.203 ± 0.009
k = "pftdpw_20"
Simulating...100%|██████████████████████████████████████| Time: 0:14:31
reward: 0.211 ± 0.009
k = "simple"
Simulating...100%|██████████████████████████████████████| Time: 0:04:27
reward: 0.089 ± 0.008
k = "pomcpow"
Simulating...100%|██████████████████████████████████████| Time: 0:11:37
reward: 0.309 ± 0.009
k = "pftdpw"
Simulating...100%|██████████████████████████████████████| Time: 0:14:31
reward: 0.214 ± 0.009
k = "despot"
Simulating...100%|██████████████████████████████████████| Time: 0:10:42
reward: 0.360 ± 0.008
saving to /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/data/multilane_Monday_30_Apr_23_41.csv...
done.
Mon 30 Apr 2018 09:53:51 PM PDT
for some reason it didn't save the journal, but in the below results, pomcpow was 0.260 and despot was .289
the csv is there somewhere
Mon 30 Apr 2018 07:31:47 PM PDT
looks like m=10 is indeed the best for pftdpw
k = "qmdp"
Simulating...100%|██████████████████████████████████████| Time: 0:13:16
reward: 0.258 ± 0.008
k = "pftdpw_5"
Simulating...100%|██████████████████████████████████████| Time: 0:15:21
reward: 0.174 ± 0.008
k = "pftdpw_20"
Simulating...100%|██████████████████████████████████████| Time: 0:14:48
reward: 0.189 ± 0.009
k = "pftdpw_15"
Simulating...100%|██████████████████████████████████████| Time: 0:14:45
reward: 0.206 ± 0.008
k = "pftdpw_50"
Simulating...100%|██████████████████████████████████████| Time: 0:20:16
reward: 0.028 ± 0.008
Mon 30 Apr 2018 08:19:37 PM PDT
zsunberg@bethpage:~/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments$ julia -p 54 multilane/compare.jl
cor = 0.75 = 0.75
lambda = 2.0 = 2.0
N = 1000 = 1000
n_iters = 1000000 = 1000000
max_time = 1.0 = 1.0
max_depth = 40 = 40
val = SimpleSolver() = Multilane.SimpleSolver()
k = "qmdp"
Simulating...100%|██████████████████████████████████████| Time: 0:13:16
reward: 0.258 ± 0.008
k = "pftdpw_5"
Simulating...100%|██████████████████████████████████████| Time: 0:15:21
reward: 0.174 ± 0.008
k = "pftdpw_20"
Simulating...100%|██████████████████████████████████████| Time: 0:14:48
reward: 0.189 ± 0.009
k = "pftdpw_15"
Simulating...100%|██████████████████████████████████████| Time: 0:14:45
reward: 0.206 ± 0.008
k = "pftdpw_50"
Simulating...100%|██████████████████████████████████████| Time: 0:20:16
reward: 0.028 ± 0.008
k = "pomcpow"
Simulating...100%|██████████████████████████████████████| Time: 0:12:55
reward: 0.260 ± 0.008
k = "simple"
Simulating...100%|██████████████████████████████████████| Time: 0:05:04
reward: 0.047 ± 0.008
k = "pftdpw"
Simulating...100%|██████████████████████████████████████| Time: 0:14:46
reward: 0.199 ± 0.008
k = "despot"
Simulating...100%|██████████████████████████████████████| Time: 0:12:23
reward: 0.289 ± 0.008
saving to /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/data/multilane_Monday_30_Apr_20
_16.csv...
done.
dangit - of course despot outperforms
Mon 30 Apr 2018 03:53:00 PM PDT
multilane comparison
data/multilane_Saturday_28_Apr_16_17.csv
Tue 06 Mar 2018 12:39:10 PM PST
zsunberg@jodhpur:~/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018$ julia -p 54 run_all.jl
LASERTAG
max_time = 1.0 = 1.0
max_depth = 90 = 90
exploration = 26.0 = 26.0
N = 1000 = 1000
k = "qmdp"
Creating Simulations...100%|████████████████████████████| Time: 0:00:20
Simulating...100%|██████████████████████████████████████| Time: 0:01:27
reward: -10.545 ± 0.195
k = "pomcpow"
Creating Simulations...100%|████████████████████████████| Time: 0:00:11
Simulating...100%|██████████████████████████████████████| Time: 0:11:34
reward: -10.324 ± 0.166
k = "pomcpdpw"
Creating Simulations...100%|████████████████████████████| Time: 0:00:11
Simulating...100%|██████████████████████████████████████| Time: 0:13:10
reward: -10.638 ± 0.189
k = "pft"
Creating Simulations...100%|████████████████████████████| Time: 0:00:12
Simulating...100%|██████████████████████████████████████| Time: 0:10:18
reward: -11.073 ± 0.161
k = "d_pomcp"
Creating Simulations...100%|████████████████████████████| Time: 0:00:11
Simulating...100%|██████████████████████████████████████| Time: 0:25:13
reward: -14.124 ± 0.210
k = "despot"
Creating Simulations...100%|████████████████████████████| Time: 0:07:10
Simulating...100%|██████████████████████████████████████| Time: 0:13:12
reward: -8.902 ± 0.186
saving to /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/lasertag_Monday
_5_Mar_20_17.csv...
done.
SIMPLELD
max_time = 1.0 = 1.0
max_depth = 20 = 20
N = 1000 = 1000
k = "heuristic_01"
Simulating...100%|██████████████████████████████████████| Time: 0:00:02
reward: 24.876 ± 0.861
k = "qmdp"
Simulating...100%|██████████████████████████████████████| Time: 0:00:09
reward: -6.369 ± 1.029
k = "pomcpdpw"
Simulating...100%|██████████████████████████████████████| Time: 0:30:01
reward: -7.263 ± 1.000
k = "pomcpow"
Simulating...100%|██████████████████████████████████████| Time: 0:03:43
reward: 56.114 ± 0.559
k = "d_pomcp"
Simulating...100%|██████████████████████████████████████| Time: 0:03:06
reward: 61.059 ± 0.396
k = "heuristic_1"
Simulating...100%|██████████████████████████████████████| Time: 0:00:01
reward: 27.612 ± 0.917
k = "despot"
Simulating...100%|██████████████████████████████████████| Time: 0:05:21
reward: -6.819 ± 1.020
k = "pomcp"
Simulating...100%|██████████████████████████████████████| Time: 0:27:20
reward: -7.102 ± 1.004
k = "side"
Simulating...100%|██████████████████████████████████████| Time: 0:00:02
reward: 42.417 ± 0.432
k = "pft"
Simulating...100%|██████████████████████████████████████| Time: 0:03:17
reward: 57.157 ± 0.468
k = "d_despot"
Simulating...100%|██████████████████████████████████████| Time: 0:08:17
reward: 54.167 ± 1.058
saving to /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/simpleld_Monday
_5_Mar_21_39.csv...
done.
SUBHUNT
[Iteration 1 ] residual: 100 | iteration runtime: 569.411 ms, ( 0.569 s total)
[Iteration 2 ] residual: 99 | iteration runtime: 571.663 ms, ( 1.14 s total)
[Iteration 3 ] residual: 98 | iteration runtime: 605.457 ms, ( 1.75 s total)
[Iteration 4 ] residual: 95.5 | iteration runtime: 591.943 ms, ( 2.34 s total)
[Iteration 5 ] residual: 90.8 | iteration runtime: 588.740 ms, ( 2.93 s total)
[Iteration 6 ] residual: 88.9 | iteration runtime: 572.866 ms, ( 3.5 s total)
[Iteration 7 ] residual: 84.4 | iteration runtime: 611.043 ms, ( 4.11 s total)
[Iteration 8 ] residual: 77.8 | iteration runtime: 604.173 ms, ( 4.72 s total)
[Iteration 9 ] residual: 73.7 | iteration runtime: 602.763 ms, ( 5.32 s total)
[Iteration 10 ] residual: 35.2 | iteration runtime: 591.217 ms, ( 5.91 s total)
[Iteration 11 ] residual: 32.7 | iteration runtime: 571.201 ms, ( 6.48 s total)
[Iteration 12 ] residual: 25.8 | iteration runtime: 572.200 ms, ( 7.05 s total)
[Iteration 13 ] residual: 6.78 | iteration runtime: 572.061 ms, ( 7.62 s total)
[Iteration 14 ] residual: 1.69 | iteration runtime: 571.424 ms, ( 8.2 s total)
[Iteration 15 ] residual: 0.272 | iteration runtime: 589.047 ms, ( 8.79 s total)
[Iteration 16 ] residual: 0.0281 | iteration runtime: 572.826 ms, ( 9.36 s total)
[Iteration 17 ] residual: 0.0018 | iteration runtime: 571.191 ms, ( 9.93 s total)
[Iteration 18 ] residual: 7.91E-05 | iteration runtime: 575.093 ms, ( 10.5 s total)
max_time = 1.0 = 1.0
max_depth = 20 = 20
N = 1000 = 1000
k = "qmdp"
Simulating...100%|██████████████████████████████████████| Time: 0:00:45
reward: 27.991 ± 1.344
k = "ping_first"
Simulating...100%|██████████████████████████████████████| Time: 0:00:32
reward: 79.014 ± 1.081
k = "pomcpow"
Simulating...100%|██████████████████████████████████████| Time: 0:04:23
reward: 69.222 ± 1.259
k = "pomcpdpw"
Simulating...100%|██████████████████████████████████████| Time: 0:05:13
reward: 28.259 ± 1.347
k = "pft"
Simulating...100%|██████████████████████████████████████| Time: 0:03:12
reward: 77.361 ± 1.109
k = "d_despot"
Simulating...100%|██████████████████████████████████████| Time: 0:03:14
reward: 27.370 ± 1.333
k = "d_pomcp"
Simulating...100%|██████████████████████████████████████| Time: 0:04:26
reward: 27.981 ± 1.344
k = "despot"
Simulating...100%|██████████████████████████████████████| Time: 0:03:16
reward: 26.814 ± 1.325
k = "pomcp"
Simulating...100%|██████████████████████████████████████| Time: 0:04:26
reward: 28.245 ± 1.347
saving to /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/subhunt_Monday_
5_Mar_22_08.csv...
done.
VDPBARRIER
max_time = 1.0 = 1.0
max_depth = 10 = 10
RO = RandomSolver = POMDPToolbox.RandomSolver
N = 1000 = 1000
k = "manage_uncertainty"
Simulating...100%|██████████████████████████████████████| Time: 0:00:10
reward: -2.733 ± 1.233
k = "pomcpow"
Simulating...100%|██████████████████████████████████████| Time: 0:06:01
reward: 29.343 ± 0.825
k = "to_next"
Simulating...100%|██████████████████████████████████████| Time: 0:00:13
reward: -17.055 ± 0.384
k = "pft"
Simulating...100%|██████████████████████████████████████| Time: 0:06:38
reward: 27.242 ± 0.839
k = "pomcpdpw"
Simulating...100%|██████████████████████████████████████| Time: 0:12:53
reward: 16.409 ± 0.957
k = "d_despot"
Simulating...100%|██████████████████████████████████████| Time: 0:13:31
reward: 14.287 ± 0.951
k = "d_pomcp"
Simulating...100%|██████████████████████████████████████| Time: 0:13:08
reward: 14.669 ± 0.946
saving to /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/vdpbarrier_Mond
ay_5_Mar_23_01.csv...
done.
FILENAMES
simpleld => /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/simpleld_Mond
ay_5_Mar_21_39.csv
subhunt => /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/subhunt_Monday
_5_Mar_22_08.csv
lasertag => /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/lasertag_Mond
ay_5_Mar_20_17.csv
vdpbarrier => /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/vdpbarrier_
Monday_5_Mar_23_01.csv
filenames = Dict("simpleld"=>"/home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/
data/simpleld_Monday_5_Mar_21_39.csv","subhunt"=>"/home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExp
eriments/icaps_2018/data/subhunt_Monday_5_Mar_22_08.csv","lasertag"=>"/home/zsunberg/.julia/v0.6/Continuo
usPOMDPTreeSearchExperiments/icaps_2018/data/lasertag_Monday_5_Mar_20_17.csv","vdpbarrier"=>"/home/zsunbe
rg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/vdpbarrier_Monday_5_Mar_23_01.csv")
Thu 01 Mar 2018 11:07:39 AM PST
zsunberg@jodhpur:~/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments$ julia -p 51 icaps_2018/vdpbarrier_table.jl
max_time = 1.0 = 1.0
max_depth = 10 = 10
RO = RandomSolver = POMDPToolbox.RandomSolver
N = 1000 = 1000
k = "manage_uncertainty"
Simulating...100%|██████████████████████████████████████| Time: 0:00:15
reward: -2.733 ± 1.233
k = "pomcpow"
Simulating...100%|██████████████████████████████████████| Time: 0:06:40
reward: 29.674 ± 0.836
k = "to_next"
Simulating...100%|██████████████████████████████████████| Time: 0:00:14
reward: -16.288 ± 0.393
k = "pft"
Simulating...100%|██████████████████████████████████████| Time: 0:07:11
reward: 28.178 ± 0.858
k = "pomcpdpw"
Simulating...100%|██████████████████████████████████████| Time: 0:13:16
reward: 17.073 ± 0.950
k = "d_despot"
Simulating...100%|██████████████████████████████████████| Time: 0:14:40
reward: 13.810 ± 0.950
k = "d_pomcp"
Simulating...100%|██████████████████████████████████████| Time: 0:14:10
reward: 12.733 ± 0.914
saving to /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/vdpbarrier_Thursday_1_Mar_19_36.csv...
Thu 01 Mar 2018 09:04:43 AM PST
pft_opt with mean reward bmdp
Simulating...100%|██████████████████████████████████████| Time: 0:20:09
mean(combined[:mean_reward]) = 27.027060837816983
iteration 32
mean(d) = [19.3555, 69.612, 22.2074, 25.485, 7.75552, 84.81]
det(cov(d)) = 0.06679763750793188
ev = eigvals(cov(d)) = [0.0124856, 0.0190092, 0.170673, 0.743723, 9.79803, 226.293]
(eigvecs(cov(d)))[:, j] = [0.842685, -0.0518326, 0.433574, -0.204976, -0.23907, 0.00620807]
(eigvecs(cov(d)))[:, j] = [-0.320754, -0.151279, -0.00913584, -0.251411, -0.899775, -0.036677]
(eigvecs(cov(d)))[:, j] = [-0.418459, 0.0591947, 0.879368, -0.0573225, 0.152303, -0.147029]
(eigvecs(cov(d)))[:, j] = [-0.0684682, -0.421876, -0.126099, -0.829423, 0.33094, -0.0629885]
(eigvecs(cov(d)))[:, j] = [-0.0758077, 0.491786, 0.0457648, -0.312836, -0.00163842, 0.807735]
(eigvecs(cov(d)))[:, j] = [0.0381511, 0.742355, -0.143687, -0.325087, -0.0230388, -0.566211]
Tue 27 Feb 2018 11:02:59 PM PST
zsunberg@jodhpur:~/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018$ julia -p 51 run_all.jl
LASERTAG
max_time = 1.0 = 1.0
max_depth = 90 = 90
exploration = 26.0 = 26.0
N = 1000 = 1000
k = "qmdp"
Creating Simulations...100%|████████████████████████████| Time: 0:00:20
Simulating...100%|██████████████████████████████████████| Time: 0:01:37
reward: -10.545 ± 0.195
k = "pomcpow"
Creating Simulations...100%|████████████████████████████| Time: 0:00:11
Simulating...100%|██████████████████████████████████████| Time: 0:12:51
reward: -10.275 ± 0.171
k = "pomcpdpw"
Creating Simulations...100%|████████████████████████████| Time: 0:00:11
Simulating...100%|██████████████████████████████████████| Time: 0:14:17
reward: -10.657 ± 0.191
k = "pft"
Creating Simulations...100%|████████████████████████████| Time: 0:00:12
Simulating...100%|██████████████████████████████████████| Time: 0:10:36
reward: -10.576 ± 0.162
k = "despot"
Creating Simulations...100%|████████████████████████████| Time: 0:08:09
Simulating...100%|██████████████████████████████████████| Time: 0:14:30
reward: -8.983 ± 0.180
k = "pomcp"
Creating Simulations...100%|████████████████████████████| Time: 0:00:29
Simulating...100%|██████████████████████████████████████| Time: 0:26:04
reward: -14.226 ± 0.211
saving to /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/lasertag_Monday
_26_Feb_18_46.csv...
done.
SIMPLELD
max_time = 1.0 = 1.0
max_depth = 20 = 20
N = 1000 = 1000
k = "heuristic_01"
Simulating...100%|██████████████████████████████████████| Time: 0:00:03
reward: 24.876 ± 0.861
k = "qmdp"
Simulating...100%|██████████████████████████████████████| Time: 0:00:11
reward: -6.369 ± 1.029
k = "pomcpdpw"
Simulating...100%|██████████████████████████████████████| Time: 0:32:21
reward: -6.940 ± 1.006
k = "pomcpow"
Simulating...100%|██████████████████████████████████████| Time: 0:03:38
reward: 57.691 ± 0.491
k = "d_pomcp"
Simulating...100%|██████████████████████████████████████| Time: 0:03:16
reward: 61.032 ± 0.395
k = "heuristic_1"
Simulating...100%|██████████████████████████████████████| Time: 0:00:02
reward: 27.612 ± 0.917
k = "despot_01"
Simulating...100%|██████████████████████████████████████| Time: 0:05:29
reward: -6.819 ± 1.020
k = "pomcp"
Simulating...100%|██████████████████████████████████████| Time: 0:28:59
reward: -6.874 ± 1.009
k = "side"
Simulating...100%|██████████████████████████████████████| Time: 0:00:02
reward: 42.417 ± 0.432
k = "pft"
Simulating...100%|██████████████████████████████████████| Time: 0:03:27
reward: 57.086 ± 0.450
k = "d_despot"
Simulating...100%|██████████████████████████████████████| Time: 0:09:18
reward: 54.609 ± 1.029
saving to /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/simpleld_Monday
_26_Feb_20_13.csv...
done.
SUBHUNT
[Iteration 1 ] residual: 100 | iteration runtime: 562.230 ms, ( 0.562 s total)
[Iteration 2 ] residual: 99 | iteration runtime: 564.119 ms, ( 1.13 s total)
[Iteration 3 ] residual: 98 | iteration runtime: 562.861 ms, ( 1.69 s total)
[Iteration 4 ] residual: 95.5 | iteration runtime: 596.654 ms, ( 2.29 s total)
[Iteration 5 ] residual: 90.8 | iteration runtime: 672.064 ms, ( 2.96 s total)
[Iteration 6 ] residual: 88.9 | iteration runtime: 633.517 ms, ( 3.59 s total)
[Iteration 7 ] residual: 84.4 | iteration runtime: 608.742 ms, ( 4.2 s total)
[Iteration 8 ] residual: 77.8 | iteration runtime: 605.443 ms, ( 4.81 s total)
[Iteration 9 ] residual: 73.7 | iteration runtime: 564.188 ms, ( 5.37 s total)
[Iteration 10 ] residual: 35.2 | iteration runtime: 596.072 ms, ( 5.97 s total)
[Iteration 11 ] residual: 32.7 | iteration runtime: 612.366 ms, ( 6.58 s total)
[Iteration 12 ] residual: 25.8 | iteration runtime: 612.047 ms, ( 7.19 s total)
[Iteration 13 ] residual: 6.78 | iteration runtime: 607.669 ms, ( 7.8 s total)
[Iteration 14 ] residual: 1.69 | iteration runtime: 560.812 ms, ( 8.36 s total)
[Iteration 15 ] residual: 0.272 | iteration runtime: 627.969 ms, ( 8.99 s total)
[Iteration 16 ] residual: 0.0281 | iteration runtime: 614.489 ms, ( 9.6 s total)
[Iteration 17 ] residual: 0.0018 | iteration runtime: 618.567 ms, ( 10.2 s total)
[Iteration 18 ] residual: 7.91E-05 | iteration runtime: 615.621 ms, ( 10.8 s total)
max_time = 1.0 = 1.0
max_depth = 20 = 20
N = 1000 = 1000
k = "qmdp"
Simulating...100%|██████████████████████████████████████| Time: 0:00:47
reward: 27.991 ± 1.344
k = "ping_first"
Simulating...100%|██████████████████████████████████████| Time: 0:00:32
reward: 79.014 ± 1.081
k = "ar_despot"
Simulating...100%|██████████████████████████████████████| Time: 0:03:22
reward: 27.002 ± 1.328
k = "pomcpow"
Simulating...100%|██████████████████████████████████████| Time: 0:04:32
reward: 69.221 ± 1.259
k = "pomcpdpw"
Simulating...100%|██████████████████████████████████████| Time: 0:05:27
reward: 27.614 ± 1.339
k = "pft"
Simulating...100%|██████████████████████████████████████| Time: 0:03:29
reward: 78.683 ± 1.056
k = "d_despot"
Simulating...100%|██████████████████████████████████████| Time: 0:03:23
reward: 27.370 ± 1.333
k = "d_pomcp"
Simulating...100%|██████████████████████████████████████| Time: 0:04:38
reward: 27.814 ± 1.342
k = "pomcp"
Simulating...100%|██████████████████████████████████████| Time: 0:04:38
reward: 28.786 ± 1.353
saving to /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/subhunt_Monday_
26_Feb_20_44.csv...
done.
VDPBARRIER
max_time = 1.0 = 1.0
max_depth = 10 = 10
RO = RandomSolver = POMDPToolbox.RandomSolver
N = 1000 = 1000
k = "manage_uncertainty"
Simulating...100%|██████████████████████████████████████| Time: 0:00:17
reward: -7.969 ± 1.331
k = "pomcpow"
Simulating...100%|██████████████████████████████████████| Time: 0:06:42
reward: 30.573 ± 0.829
k = "to_next"
Simulating...100%|██████████████████████████████████████| Time: 0:00:25
reward: -17.279 ± 0.317
k = "pft"
Simulating...100%|██████████████████████████████████████| Time: 0:08:12
reward: 23.208 ± 0.896
k = "pomcpdpw"
Simulating...100%|██████████████████████████████████████| Time: 0:13:46
reward: 16.035 ± 0.966
k = "d_despot"
Simulating...100%|██████████████████████████████████████| Time: 0:14:49
reward: 12.789 ± 0.926
k = "d_pomcp"
Simulating...100%|██████████████████████████████████████| Time: 0:13:42
reward: 16.108 ± 0.984
saving to /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/bdpbarrier_Mond
ay_26_Feb_21_42.csv...
done.
SUBHUNT_DISCRETIZATION.JL
max_depth = 20 = 20
max_time = 1.0 = 1.0
N = 1000 = 1000
max_time = 1.0
(k, binsize) = ("qmdp", 0.01)
Simulating...100%|██████████████████████████████████████| Time: 0:00:46
reward: 27.991 ± 1.344
(k, binsize) = ("ping_first", 0.01)
Simulating...100%|██████████████████████████████████████| Time: 0:00:33
reward: 79.014 ± 1.081
(k, binsize) = ("pomcpow", 0.01)
Simulating...100%|██████████████████████████████████████| Time: 0:04:38
reward: 69.147 ± 1.258
(k, binsize) = ("d_despot", 0.01)
Simulating...100%|██████████████████████████████████████| Time: 0:03:20
reward: 28.926 ± 1.354
(k, binsize) = ("d_pomcp", 0.01)
Simulating...100%|██████████████████████████████████████| Time: 0:04:37
reward: 27.986 ± 1.344
(k, binsize) = ("qmdp", 0.03162277660168379)
Simulating...100%|██████████████████████████████████████| Time: 0:00:46
reward: 27.991 ± 1.344
(k, binsize) = ("ping_first", 0.03162277660168379)
Simulating...100%|██████████████████████████████████████| Time: 0:00:33
reward: 79.014 ± 1.081
(k, binsize) = ("pomcpow", 0.03162277660168379)
Simulating...100%|██████████████████████████████████████| Time: 0:04:38
reward: 69.297 ± 1.254
(k, binsize) = ("d_despot", 0.03162277660168379)
Simulating...100%|██████████████████████████████████████| Time: 0:03:21
reward: 26.500 ± 1.322
(k, binsize) = ("d_pomcp", 0.03162277660168379)
Simulating...100%|██████████████████████████████████████| Time: 0:04:37
reward: 28.334 ± 1.348
(k, binsize) = ("qmdp", 0.1)
Simulating...100%|██████████████████████████████████████| Time: 0:00:46
reward: 27.991 ± 1.344
(k, binsize) = ("ping_first", 0.1)
Simulating...100%|██████████████████████████████████████| Time: 0:00:33
reward: 79.014 ± 1.081
(k, binsize) = ("pomcpow", 0.1)
Simulating...100%|██████████████████████████████████████| Time: 0:04:35
reward: 68.457 ± 1.275
(k, binsize) = ("d_despot", 0.1)
Simulating...100%|██████████████████████████████████████| Time: 0:03:21
reward: 26.690 ± 1.325
(k, binsize) = ("d_pomcp", 0.1)
Simulating...100%|██████████████████████████████████████| Time: 0:04:37
reward: 28.067 ± 1.344
(k, binsize) = ("qmdp", 0.31622776601683794)
Simulating...100%|██████████████████████████████████████| Time: 0:00:46
reward: 27.991 ± 1.344
(k, binsize) = ("ping_first", 0.31622776601683794)
Simulating...100%|██████████████████████████████████████| Time: 0:00:33
reward: 79.014 ± 1.081
(k, binsize) = ("pomcpow", 0.31622776601683794)
Simulating...100%|██████████████████████████████████████| Time: 0:04:39
reward: 67.440 ± 1.289
(k, binsize) = ("d_despot", 0.31622776601683794)
Simulating...100%|██████████████████████████████████████| Time: 0:03:22
reward: 25.947 ± 1.314
(k, binsize) = ("d_pomcp", 0.31622776601683794)
Simulating...100%|██████████████████████████████████████| Time: 0:04:37
reward: 27.978 ± 1.343
(k, binsize) = ("qmdp", 1.0)
Simulating...100%|██████████████████████████████████████| Time: 0:00:46
reward: 27.991 ± 1.344
(k, binsize) = ("ping_first", 1.0)
Simulating...100%|██████████████████████████████████████| Time: 0:00:33
reward: 79.014 ± 1.081
(k, binsize) = ("pomcpow", 1.0)
Simulating...100%|██████████████████████████████████████| Time: 0:04:40
reward: 66.194 ± 1.310
(k, binsize) = ("d_despot", 1.0)
Simulating...100%|██████████████████████████████████████| Time: 0:03:22
reward: 27.184 ± 1.330
(k, binsize) = ("d_pomcp", 1.0)
Simulating...100%|██████████████████████████████████████| Time: 0:04:37
reward: 28.521 ± 1.350
(k, binsize) = ("qmdp", 3.1622776601683795)
Simulating...100%|██████████████████████████████████████| Time: 0:00:46
reward: 27.991 ± 1.344
(k, binsize) = ("ping_first", 3.1622776601683795)
Simulating...100%|██████████████████████████████████████| Time: 0:00:33
reward: 79.014 ± 1.081
(k, binsize) = ("pomcpow", 3.1622776601683795)
Simulating...100%|██████████████████████████████████████| Time: 0:04:38
reward: 68.064 ± 1.278
(k, binsize) = ("d_despot", 3.1622776601683795)
Simulating...100%|██████████████████████████████████████| Time: 0:03:25
reward: 26.223 ± 1.318
(k, binsize) = ("d_pomcp", 3.1622776601683795)
Simulating...100%|██████████████████████████████████████| Time: 0:04:37
reward: 28.174 ± 1.346
(k, binsize) = ("qmdp", 10.0)
Simulating...100%|██████████████████████████████████████| Time: 0:00:46
reward: 27.991 ± 1.344
(k, binsize) = ("ping_first", 10.0)
Simulating...100%|██████████████████████████████████████| Time: 0:00:33
reward: 79.014 ± 1.081
(k, binsize) = ("pomcpow", 10.0)
Simulating...100%|██████████████████████████████████████| Time: 0:05:00
reward: 44.517 ± 1.455
(k, binsize) = ("d_despot", 10.0)
Simulating... 92%|███████████████████████████████████ | ETA: 0:00:19^[[B
Simulating...100%|██████████████████████████████████████| Time: 12:20:46
reward: 26.903 ± 1.329
(k, binsize) = ("d_pomcp", 10.0)
Simulating... 30%|████████████ | ETA: 0:03:21
Simulating...100%|██████████████████████████████████████| Time: 0:05:37
reward: 28.562 ± 1.355
saving to /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/subhunt_discret
ization_Tuesday_27_Feb_11_38.csv...
done.
LD_DISCRETIZATION.JL
max_time = 1.0 = 1.0
max_depth = 20 = 20
N = 1000 = 1000
(k, d) = ("qmdp", 0.01)
Simulating...100%|██████████████████████████████████████| Time: 0:00:11
reward: -6.369 ± 1.029
(k, d) = ("side", 0.01)
Simulating...100%|██████████████████████████████████████| Time: 0:00:02
reward: 42.417 ± 0.432
(k, d) = ("pomcpow", 0.01)
Simulating...100%|██████████████████████████████████████| Time: 0:04:15
reward: 59.594 ± 0.409
(k, d) = ("d_pomcp", 0.01)
Simulating...100%|██████████████████████████████████████| Time: 0:03:41
reward: 57.645 ± 0.445
(k, d) = ("d_despot", 0.01)
Simulating...100%|██████████████████████████████████████| Time: 0:08:10
reward: -28.822 ± 2.424
(k, d) = ("qmdp", 0.03162277660168379)
Simulating...100%|██████████████████████████████████████| Time: 0:00:11
reward: -6.369 ± 1.029
(k, d) = ("side", 0.03162277660168379)
Simulating...100%|██████████████████████████████████████| Time: 0:00:01
reward: 42.417 ± 0.432
(k, d) = ("pomcpow", 0.03162277660168379)
Simulating...100%|██████████████████████████████████████| Time: 0:03:27
reward: 59.297 ± 0.414
(k, d) = ("d_pomcp", 0.03162277660168379)
Simulating...100%|██████████████████████████████████████| Time: 0:03:07
reward: 60.991 ± 0.412
(k, d) = ("d_despot", 0.03162277660168379)
Simulating...100%|██████████████████████████████████████| Time: 0:08:07
reward: -12.637 ± 2.401
(k, d) = ("qmdp", 0.1)
Simulating...100%|██████████████████████████████████████| Time: 0:00:11
reward: -6.369 ± 1.029
(k, d) = ("side", 0.1)
Simulating...100%|██████████████████████████████████████| Time: 0:00:01
reward: 42.417 ± 0.432
(k, d) = ("pomcpow", 0.1)
Simulating...100%|██████████████████████████████████████| Time: 0:03:33
reward: 58.598 ± 0.427
(k, d) = ("d_pomcp", 0.1)
Simulating...100%|██████████████████████████████████████| Time: 0:05:11
reward: 53.900 ± 0.771
(k, d) = ("d_despot", 0.1)
Simulating...100%|██████████████████████████████████████| Time: 0:08:53
reward: 2.588 ± 2.252
(k, d) = ("qmdp", 0.31622776601683794)
Simulating...100%|██████████████████████████████████████| Time: 0:00:11
reward: -6.369 ± 1.029
(k, d) = ("side", 0.31622776601683794)
Simulating...100%|██████████████████████████████████████| Time: 0:00:01
reward: 42.417 ± 0.432
(k, d) = ("pomcpow", 0.31622776601683794)
Simulating...100%|██████████████████████████████████████| Time: 0:03:36
reward: 58.141 ± 0.428
(k, d) = ("d_pomcp", 0.31622776601683794)
Simulating...100%|██████████████████████████████████████| Time: 0:28:46
reward: -9.846 ± 0.824
(k, d) = ("d_despot", 0.31622776601683794)
Simulating...100%|██████████████████████████████████████| Time: 0:08:34
reward: 14.237 ± 2.115
(k, d) = ("qmdp", 1.0)
Simulating...100%|██████████████████████████████████████| Time: 0:00:11
reward: -6.369 ± 1.029
(k, d) = ("side", 1.0)
Simulating...100%|██████████████████████████████████████| Time: 0:00:01
reward: 42.417 ± 0.432
(k, d) = ("pomcpow", 1.0)
Simulating...100%|██████████████████████████████████████| Time: 0:04:07
reward: 53.684 ± 0.515
(k, d) = ("d_pomcp", 1.0)
Simulating...100%|██████████████████████████████████████| Time: 0:30:29
reward: -11.405 ± 0.838
(k, d) = ("d_despot", 1.0)
Simulating...100%|██████████████████████████████████████| Time: 0:08:32
reward: 53.812 ± 1.071
(k, d) = ("qmdp", 3.1622776601683795)
Simulating...100%|██████████████████████████████████████| Time: 0:00:11
reward: -6.369 ± 1.029
(k, d) = ("side", 3.1622776601683795)
Simulating...100%|██████████████████████████████████████| Time: 0:00:01
reward: 42.417 ± 0.432
(k, d) = ("pomcpow", 3.1622776601683795)
Simulating...100%|██████████████████████████████████████| Time: 0:04:20
reward: 52.095 ± 0.579
(k, d) = ("d_pomcp", 3.1622776601683795)
Simulating...100%|██████████████████████████████████████| Time: 0:30:51
reward: -13.575 ± 0.707
(k, d) = ("d_despot", 3.1622776601683795)
Simulating...100%|██████████████████████████████████████| Time: 0:08:56
reward: 54.556 ± 1.112
(k, d) = ("qmdp", 10.0)
Simulating...100%|██████████████████████████████████████| Time: 0:00:11
reward: -6.369 ± 1.029
(k, d) = ("side", 10.0)
Simulating...100%|██████████████████████████████████████| Time: 0:00:01
reward: 42.417 ± 0.432
(k, d) = ("pomcpow", 10.0)
Simulating...100%|██████████████████████████████████████| Time: 0:06:18
reward: 40.621 ± 0.911
(k, d) = ("d_pomcp", 10.0)
Simulating... 32%|████████████ | ETA: 0:22:05
Simulating...100%|██████████████████████████████████████| Time: 0:32:13
reward: -16.725 ± 0.536
(k, d) = ("d_despot", 10.0)
Simulating...100%|██████████████████████████████████████| Time: 0:08:18
reward: 39.906 ± 1.656
saving to /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/ld_discretizati
on_Tuesday_27_Feb_15_23.csv...
done.
FILENAMES
ld_discretization.jl => /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/l
d_discretization_Tuesday_27_Feb_15_23.csv
simpleld => /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/simpleld_Mond
ay_26_Feb_20_13.csv
subhunt => /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/subhunt_Monday
_26_Feb_20_44.csv
lasertag => /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/lasertag_Mond
ay_26_Feb_18_46.csv
subhunt_discretization.jl => /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/d
ata/subhunt_discretization_Tuesday_27_Feb_11_38.csv
vdpbarrier => /home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/bdpbarrier_
Monday_26_Feb_21_42.csv
filenames = Dict("ld_discretization.jl"=>"/home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments
/icaps_2018/data/ld_discretization_Tuesday_27_Feb_15_23.csv","simpleld"=>"/home/zsunberg/.julia/v0.6/Cont
inuousPOMDPTreeSearchExperiments/icaps_2018/data/simpleld_Monday_26_Feb_20_13.csv","subhunt"=>"/home/zsun
berg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/subhunt_Monday_26_Feb_20_44.csv","l
asertag"=>"/home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/lasertag_Monda
y_26_Feb_18_46.csv","subhunt_discretization.jl"=>"/home/zsunberg/.julia/v0.6/ContinuousPOMDPTreeSearchExp
eriments/icaps_2018/data/subhunt_discretization_Tuesday_27_Feb_11_38.csv","vdpbarrier"=>"/home/zsunberg/.
julia/v0.6/ContinuousPOMDPTreeSearchExperiments/icaps_2018/data/bdpbarrier_Monday_26_Feb_21_42.csv")
Mon 26 Feb 2018 09:47:36 AM PST
pow_opt after barrier fix
iteration 16
mean(d) = [120.406, 28.0466, 30.7495, 5.46869, 114.904]
det(cov(d)) = 1.061947260056586e6
ev = eigvals(cov(d)) = [0.431383, 4.18512, 9.4225, 136.406, 457.648]
(eigvecs(cov(d)))[:, j] = [-0.0766408, -0.176956, -0.120116, 0.969829, 0.0884104]
(eigvecs(cov(d)))[:, j] = [0.345486, 0.86682, 0.242012, 0.227918, -0.136915]
(eigvecs(cov(d)))[:, j] = [-0.231256, 0.394253, -0.82353, -0.0781248, 0.326773]
(eigvecs(cov(d)))[:, j] = [0.0616476, 0.02729, 0.366556, -0.0293004, 0.927488]
(eigvecs(cov(d)))[:, j] = [0.904149, -0.247244, -0.338286, -0.0228664, 0.0801513]
creating 150 simulation sets......................................................................................................................................................
Simulating...100%|██████████████████████████████████████| Time: 0:20:18
mean(combined[:mean_reward]) = 28.22687967793777
Mon 26 Feb 2018 12:15:25 AM PST
pft_opt after barrier fix
Simulating...100%|██████████████████████████████████████| Time: 0:23:25
mean(combined[:mean_reward]) = 22.285554134050294
iteration 64
mean(d) = [14.0333, 86.7138, 18.553, 21.4697, 8.23664, 64.7801]
det(cov(d)) = 3.642743280683159e-11
ev = eigvals(cov(d)) = [7.02895e-6, 5.05806e-5, 0.00484179, 0.684519, 0.985454, 31.3709]
(eigvecs(cov(d)))[:, j] = [0.715766, 0.0888766, 0.393591, 0.112399, 0.554153, -0.0717465]
(eigvecs(cov(d)))[:, j] = [-0.605319, -0.00852499, 0.00964775, -0.0374598, 0.792353, 0.0647773]
(eigvecs(cov(d)))[:, j] = [0.324778, -0.152769, -0.897078, -0.0392837, 0.253444, 0.0256003]
(eigvecs(cov(d)))[:, j] = [0.0453787, 0.360066, -0.00923739, -0.871419, 0.0243529, -0.329004]
(eigvecs(cov(d)))[:, j] = [-0.062576, 0.892638, -0.197159, 0.394053, -0.011388, -0.0707365]
(eigvecs(cov(d)))[:, j] = [0.0990585, 0.205526, 0.0358774, -0.264142, -0.0115871, 0.936351]
Sat 24 Feb 2018 11:00:46 PM PST
After barrier fix
max_time = 1.0 = 1.0
max_depth = 10 = 10
RO = RandomSolver = POMDPToolbox.RandomSolver
N = 1000 = 1000
k = "manage_uncertainty"
Simulating...100%|██████████████████████████████████████| Time: 0:02:20
reward: -6.606 ± 1.328
k = "pomcpow"
Simulating...100%|██████████████████████████████████████| Time: 0:08:12
reward: 29.205 ± 0.855
k = "to_next"
Simulating...100%|██████████████████████████████████████| Time: 0:03:55
reward: -17.460 ± 0.283
k = "pft"
Simulating...100%|██████████████████████████████████████| Time: 0:09:43
reward: 23.968 ± 0.918
k = "pomcpdpw"
Simulating...100%|██████████████████████████████████████| Time: 0:14:46
reward: 15.003 ± 0.961
k = "d_despot"
Simulating...100%|██████████████████████████████████████| Time: 0:16:22
reward: 10.161 ± 0.904
k = "d_pomcp"
Simulating...100%|██████████████████████████████████████| Time: 0:14:35
reward: 14.884 ± 0.960
Sat 24 Feb 2018 09:13:31 PM PST
pft_opt for Random vdp tag before barrier fix
mean(combined[:mean_reward]) = 19.881941923853947
iteration 16
mean(d) = [13.7315, 75.8478, 21.2246, 21.1542, 7.64275, 63.8986]
det(cov(d)) = 138465.70897763397
ev = eigvals(cov(d)) = [0.174967, 1.61616, 3.06432, 20.7234, 62.6792, 123.023]
(eigvecs(cov(d)))[:, j] = [0.149519, 0.0428675, 0.138351, -0.135943, 0.968548, 0.0100085]
(eigvecs(cov(d)))[:, j] = [-0.883995, -0.0257854, -0.381891, 0.153053, 0.213053, 0.0568334]
(eigvecs(cov(d)))[:, j] = [-0.44218, 0.053969, 0.792449, -0.371542, -0.0978066, -0.161181]
(eigvecs(cov(d)))[:, j] = [-0.00153459, -0.361093, 0.356489, 0.81028, 0.0819346, -0.281527]
(eigvecs(cov(d)))[:, j] = [-0.0250064, -0.0159634, 0.271169, 0.201888, -0.0155503, 0.940525]
(eigvecs(cov(d)))[:, j] = [0.00677043, -0.929485, -0.0801613, -0.35034, 0.00151613, 0.0827431]
Sat 24 Feb 2018 02:07:48 PM PST
pow_opt for Random vdp tag
Simulating...100%|██████████████████████████████████████| Time: 0:21:48
mean(combined[:mean_reward]) = 25.774977146997575
iteration 48
mean(d) = [86.898, 26.0576, 28.4142, 6.85923, 105.764]
det(cov(d)) = 2.3484144732571604e-5
ev = eigvals(cov(d)) = [0.000163307, 0.00109723, 0.157391, 10.6971, 77.8439]
(eigvecs(cov(d)))[:, j] = [-0.101314, -0.00720456, 0.612271, 0.782309, -0.0529097]
(eigvecs(cov(d)))[:, j] = [0.133097, -0.34949, -0.721847, 0.581291, 0.0343396]
(eigvecs(cov(d)))[:, j] = [-0.322572, -0.879036, 0.212012, -0.202759, 0.192828]
(eigvecs(cov(d)))[:, j] = [-0.931578, 0.257634, -0.243062, 0.0689728, -0.0441557]
(eigvecs(cov(d)))[:, j] = [0.0113835, 0.196787, 0.00569271, 0.0649899, 0.978207]
Fri 23 Feb 2018 07:08:26 PM PST
max_time = 1.0 = 1.0
max_depth = 10 = 10
RO = RandomSolver = POMDPToolbox.RandomSolver
N = 1000 = 1000
k = "manage_uncertainty"
Simulating...100%|██████████████████████████████████████| Time: 0:02:55
reward: -21.496 ± 1.089
k = "pomcpow"
Simulating...100%|██████████████████████████████████████| Time: 0:08:55
reward: 27.340 ± 0.807
k = "to_next"
Simulating...100%|██████████████████████████████████████| Time: 0:04:04
reward: -18.300 ± 0.240
k = "pft"
Simulating...100%|██████████████████████████████████████| Time: 0:10:36
reward: 21.398 ± 0.905
k = "pomcpdpw"
Simulating...100%|██████████████████████████████████████| Time: 0:14:32
reward: 15.805 ± 0.914
k = "d_despot"
Simulating...100%|██████████████████████████████████████| Time: 0:17:32
reward: 8.548 ± 0.862
k = "d_pomcp"
Simulating...100%|██████████████████████████████████████| Time: 0:15:56
reward: 12.237 ± 0.877
Fri 23 Feb 2018 12:35:24 PM PST
With barriers (!!) random rollouts
max_time = 1.0 = 1.0
max_depth = 10 = 10
RO = RandomSolver = POMDPToolbox.RandomSolver
N = 1000 = 1000
k = "manage_uncertainty"
Simulating...100%|██████████████████████████████████████| Time: 0:02:56
reward: -21.496 ± 1.089
k = "pomcpow"
Simulating...100%|██████████████████████████████████████| Time: 0:08:50
reward: 27.484 ± 0.820
k = "pft"
Simulating...100%|██████████████████████████████████████| Time: 0:11:55
reward: 20.522 ± 0.905
With barriers, tonext rollouts
RO = ToNextMLSolver = VDPTag2.ToNextMLSolver
N = 1000 = 1000
k = "manage_uncertainty"
Simulating...100%|██████████████████████████████████████| Time: 0:02:55
reward: -21.496 ± 1.089
k = "pomcpow"
Simulating...100%|██████████████████████████████████████| Time: 0:16:20
reward: 17.485 ± 0.987
k = "pft"
Simulating...100%|██████████████████████████████████████| Time: 0:12:31
reward: 17.525 ± 0.956
Thu 22 Feb 2018 10:58:34 PM PST
mean(combined[:mean_reward]) = 35.92344192575698
iteration 16
mean(d) = [100.335, 25.5375, 19.8407, 5.45474, 107.777]
det(cov(d)) = 1703.1403575426195
ev = eigvals(cov(d)) = [0.119598, 0.404508, 2.79202, 91.3478, 138.034]
(eigvecs(cov(d)))[:, j] = [0.0705534, 0.655333, -0.258949, -0.66055, 0.249359]
(eigvecs(cov(d)))[:, j] = [0.115753, 0.589482, -0.214097, 0.748976, 0.17975]
(eigvecs(cov(d)))[:, j] = [-0.128394, -0.326238, -0.935792, 0.0170253, -0.0329793]
(eigvecs(cov(d)))[:, j] = [-0.978743, 0.184658, 0.071931, 0.0358061, -0.0388228]
(eigvecs(cov(d)))[:, j] = [-0.0848558, -0.287263, 0.0789144, 0.0337159, 0.950219]
Thu 22 Feb 2018 04:03:52 PM PST
!! With sampled rollouts
RO = RandomSolver = POMDPToolbox.RandomSolver
N = 1000 = 1000
k = "pomcpow"
Simulating...100%|██████████████████████████████████████| Time: 0:06:54
reward: 35.952 ± 0.756
k = "pft"
Simulating...100%|██████████████████████████████████████| Time: 0:08:46
reward: 29.060 ± 0.871
Wed 21 Feb 2018 06:02:02 PM PST
PFT opt with single sample rollouts (uh oh)
Simulating...100%|██████████████████████████████████████| Time: 0:19:57
mean(combined[:mean_reward]) = 29.854269742495255
iteration 43
mean(d) = [15.2746, 86.8576, 21.0007, 20.7577, 5.59402, 54.8343]
det(cov(d)) = 2.1345807713889652e-6
ev = eigvals(cov(d)) = [0.00123411, 0.00433508, 0.0496962, 0.257932, 1.3166, 23.6417]
(eigvecs(cov(d)))[:, j] = [0.75881, 0.0841258, -0.227082, 0.218579, -0.517278, -0.224077]
(eigvecs(cov(d)))[:, j] = [0.578282, 0.295266, 0.224886, -0.0956049, 0.712911, 0.102233]
(eigvecs(cov(d)))[:, j] = [0.0420975, 0.418438, 0.28644, -0.527804, -0.442631, 0.516323]
(eigvecs(cov(d)))[:, j] = [-0.0391215, 0.200461, -0.889522, -0.114156, 0.162792, 0.357075]
(eigvecs(cov(d)))[:, j] = [0.0722885, -0.192936, -0.154998, -0.797661, 0.0411566, -0.543662]
(eigvecs(cov(d)))[:, j] = [-0.285077, 0.808233, -0.0231928, 0.123331, -0.00799412, -0.499678]
Wed 21 Feb 2018 06:01:12 PM PST
PFT opt with full belief rollouts
mean(combined[:mean_reward]) = 24.406039463499653
iteration 16
mean(d) = [10.0884, 64.2481, 16.4526, 13.9518, 4.77987, 58.2047]
det(cov(d)) = 221.66408916796289
ev = eigvals(cov(d)) = [0.0985605, 0.477854, 0.728103, 2.75178, 41.7087, 56.3202]
(eigvecs(cov(d)))[:, j] = [-0.200634, -0.0551381, -0.315826, 0.0957057, -0.920166, -0.0330813]
(eigvecs(cov(d)))[:, j] = [-0.643877, -0.114834, 0.213639, 0.702378, 0.150693, -0.102722]
(eigvecs(cov(d)))[:, j] = [0.0293745, -0.101649, 0.915678, -0.19415, -0.335313, 0.0144812]
(eigvecs(cov(d)))[:, j] = [0.70431, 0.0218577, 0.0774332, 0.675213, -0.117224, 0.166794]
(eigvecs(cov(d)))[:, j] = [0.178615, -0.873455, -0.0822278, -0.0302184, 0.0543308, -0.441075]
(eigvecs(cov(d)))[:, j] = [-0.127867, -0.458301, -0.0582189, -0.0546492, 0.0381803, 0.875087]
creating 120 simulation sets........................................................................................................................
Tue 20 Feb 2018 10:14:41 AM PST
Simulating...100%|██████████████████████████████████████| Time: 0:36:48
mean(combined[:mean_reward]) = 44.49592403115041
iteration 16
mean(d) = [11.0231, 72.3433, 25.3049, 16.8478, 27.5656, 1.58413]
det(cov(d)) = 37.838496819420826
ev = eigvals(cov(d)) = [0.00731722, 0.807921, 1.68545, 3.97169, 7.97678, 119.867]
(eigvecs(cov(d)))[:, j] = [-0.00243827, -0.00628171, -0.0748551, -0.0522666, 0.055383, 0.99426]
(eigvecs(cov(d)))[:, j] = [-0.166587, -0.0497107, 0.88766, -0.206226, -0.365498, 0.0756252]
(eigvecs(cov(d)))[:, j] = [0.283243, -0.223624, 0.178723, -0.618369, 0.672435, -0.0572258]
(eigvecs(cov(d)))[:, j] = [-0.463711, -0.548425, 0.17972, 0.511272, 0.436317, 0.0115012]
(eigvecs(cov(d)))[:, j] = [-0.822642, 0.227429, -0.225106, -0.465093, 0.0504669, -0.0447885]
(eigvecs(cov(d)))[:, j] = [0.0157834, -0.771355, -0.30256, -0.30765, -0.467179, -0.0177631]