Bot implementation by http://2018.russianaicup.ru/profile/elsid .
Build and run using local runner vs helper:
scripts/run_vs_helper.shStrategy work for all robots at once. Actions are calculating once per game tick for all robots. Possible action has restrictions depending on a robot role. There are following stages:
- Assign roles for robots.
- Set robots priority.
- Give orders for robots.
Each order contains an action which applies to current robot.
There are only two roles: Forward and Goalkeeper. For games with single robot there is only one Forward. Games with two or more robots has one Goalkeeper and the rest are Forwards. The difference between roles are where robots can play, when they could quit roles to change it, and what orders they can perform. Roles assignment is base on combination which has max total score by role score function. If robot can't quit role then role score for new role is 0.
Plays only in area with z coordinate less than (arena.corner_radius + world.rules.BALL_RADIUS) * goalkeeper_max_z_factor.
Goalkeeper can quit role only when there is a teammate in allowed for goalkeeper area which has less position by z.
Score depends on distance to my goal.
Has no restrictions. Score depends on distance to ball and opponent goal.
Initially robots are ordered by distance to ball.
If there is existing order, each robots gets priority penalty robot_priority_change_gap * number to lower switch frequency.
Orders are predefined scenarios for robots. Each order means some action to reach some goal in the future. There are following orders types:
Idle- do nothing when there is no other applicable order;Play- generated order by optimal planning;WalkToGoalkeeperPosition- default Goakeeper's order;TakeNitroPack- go to nearest nitro pack;PushOpponent- go to the center of nearest opponent with enabled nitro.
Based on strategy simulation implementation. Now supports fast simulation tests to check goalkeeper quality (percent of safes). Allows to generate situations with random ball position and velocity. Uses this situations to check goalkeeper strategy can it safe goal from ball in 150 game ticks. Simulations contains only arena, ball and goalkeeper. No other robots are included.
Usage:
- Build simulation tool:
cargo build --features disable_output,use_single_goalkeeper --release --bin simulation_tool- Generate goal situations:
target/release/simulation_tool generate_empty 10000 > empty.json- Filter hits (empty.json contains all results with goals and without):
scripts/filter_simulations.py hit < empty.json > hits.json- Check goalkeeper:
target/release/simulation_tool check_goalkeeper < hits.json > goalkeeper.json- Join goakeeper results with empty (if need to have full statistics):
scripts/join_simulations.py goalkeeper.json empty.json > empty_and_goalkeeper.json- See check goalkeeper report script how to get visual results.
scripts/stats_report.py produces report with graphics and tables base on enable_stats features.
Usage:
- Build strategy with
--features=enable_stats
cargo build --features=enable_stats- Run it using local runner:
target/release/my-strategy 127.0.0.1 ${PORT} 0000000000000000 > stats.json- Build report:
scripts/stats_report.py < stats.jsonExample:
scripts/stats_report.py < log/stats.1549829457.json
n sum q95 min max mean median stdev
player_id 40879 74975 2.0 1 2 1.834071283544118 2 0.3720211863681954
robot_id 40879 130200 4.0 1 4 3.185009418038602 3 0.8643186159515917
current_tick 40879 366519610 17157.0 0 17999 8965.963208493358 9043 5290.7518385241665
order 3 - - idle (80) play (31131)
time_to_jump 40879 10654.26666666669 1.1333333333333342 0.0 1.6666666666666656 0.26062933698639085 0.0 0.4064722516234682
time_to_watch 40879 12389.666666666726 1.2333333333333338 0.0 1.6666666666666656 0.30308145176414963 0.03333333333333333 0.4244621053586586
time_to_end 40879 49303.51666665346 1.6666666666666656 0.0 1.6666666666666656 1.2060842160196346 1.6666666666666656 0.7120357255180221
time_to_score 4584 5071.366666666668 1.6333333333333324 0.016666666666666666 1.6666666666666656 1.1063190808609658 1.2000000000000006 0.43295545202682695
iteration 40879 3758551 439.0 0 4914 91.94332053132415 2 224.15174096247264
total_iterations 40879 12305431 1264.0 0 6859 301.02084199711345 5 581.3825766074057
game_score 40879 1520 1.0 -1 1 0.03718290564837692 0 0.33280017479505514
order_score 40879 30911349 2634.0 -1234 3203 756.1669561388488 778 804.3125921954689
path_micro_ticks 40879 24264343 2500.0 0 2800 593.5649844663519 300 785.8256499248528
plan_micro_ticks 40879 1096339133 104847.19999999995 0 519391 26819.127987475233 3328 50746.73682799437
game_micro_ticks 40879 17244133289278 1044972509.4 0 1120780908 421833540.1863549 381218969 367894391.70325005
game_micro_ticks_limit 40879 2825881150000000 169830999999.99997 0 180010000000 69127942219.72162 61460000000 60642987790.03748
current_step 40879 17696 1.0 0 1 0.4328873015484723 0 0.4954814742059794
reached_game_limit 40879 0 0.0 0 0 0 0 0.0
reached_plan_limit 40879 83 0.0 0 1 0.002030382347904792 0 0.04501454724747978
reached_path_limit 40879 0 0.0 0 0 0 0 0.0
other_number 40879 17459 2.0 0 2 0.4270897037598767 0 0.628638746861726
ticks_with_near_micro_ticks 40879 1534988 100.0 0 116 37.54954866802026 26 36.70976257562621
ticks_with_far_micro_ticks 40879 2265277 100.0 0 120 55.414197998972575 81 46.094717172448505
path 26 - -fork_ball,walk_to_ball,push_ball,push_ball,jump,watch_me_jump,watch_ball_move (1)fork_ball,walk_to_position,jump,watch_me_jump,watch_ball_move (11881)
transitions 12 - - push_robot (1) watch_me_jump (29080)
scripts/site_report.py shows statistics report based on site games.
Usage:
scripts/site_report.py --help
Usage: site_report.py [OPTIONS]
Options:
--profile TEXT Player profile
--opponent TEXT Opponent profile (default: all)
--first_page INTEGER First pages to fetch
--last_page INTEGER Last pages to fetch
--first_game_id INTEGER First game id to fetch
--last_game_id INTEGER Last game id to fetch
--version INTEGER Version to check (default: all)
--sort_by TEXT Sort by field
--creator TEXT Game creator (default: all)
--help Show this message and exit.
Example:
scripts/site_report.py --first_page=1 --last_page=3 --sort_by=mean_place --creator=System
game_type n total_score mean_score median_score mean_place median_place
2x3+ 15 129 8.6 7 1.4 1
2x2+ 6 25 4.166666666666667 3.5 1.5 1.5
2x2 9 57 6.333333333333333 6 1.5555555555555556 2
total 30 211 7.033333333333333 5.5 1.4666666666666666 1.0
opponent n total_score mean_score median_score mean_place median_place
robostac 1 16 16 16 1 1
ykaland 1 5 5 5 1 1
sergio-dna 1 12 12 12 1 1
MucmuK 1 13 13 13 1 1
FunnyHouse 1 23 23 23 1 1
dedoo 2 16 8 8.0 1 1.0
iam1 1 18 18 18 1 1
RiSuS 1 7 7 7 1 1
discourage 1 11 11 11 1 1
concretemixer 1 9 9 9 1 1
cas 5 32 6.4 8 1.4 1
mixei4 2 10 5 5.0 1.5 1.5
lama 3 12 4 4 1.6666666666666667 2
Enchante_ 1 3 3 3 2 2
Daramant 2 3 1.5 1.5 2 2.0
Antmsu 1 4 4 4 2 2
giperball 3 7 2.3333333333333335 2 2 2
Lev 1 2 2 2 2 2
evil_homura 1 8 8 8 2 2
total 30 211 7.033333333333333 5.5 1.4666666666666666 1.0
opponent version n total_score mean_score median_score mean_place median_place
robostac 127 1 16 16 16 1 1
ykaland 28 1 5 5 5 1 1
sergio-dna 33 1 12 12 12 1 1
MucmuK 17 1 13 13 13 1 1
FunnyHouse 37 1 23 23 23 1 1
dedoo 15 2 16 8 8.0 1 1.0
iam1 26 1 18 18 18 1 1
RiSuS 63 1 7 7 7 1 1
discourage 54 1 11 11 11 1 1
concretemixer 51 1 9 9 9 1 1
cas 26 5 32 6.4 8 1.4 1
mixei4 27 2 10 5 5.0 1.5 1.5
lama 22 3 12 4 4 1.6666666666666667 2
Enchante_ 20 1 3 3 3 2 2
Daramant 21 2 3 1.5 1.5 2 2.0
Antmsu 54 1 4 4 4 2 2
giperball 88 3 7 2.3333333333333335 2 2 2
Lev 23 1 2 2 2 2 2
evil_homura 29 1 8 8 8 2 2
total 30 211 7.033333333333333 5.5 1.4666666666666666 1.0
opponent game_type n total_score mean_score median_score mean_place median_place
robostac 2x3+ 1 16 16 16 1 1
ykaland 2x2+ 1 5 5 5 1 1
sergio-dna 2x2 1 12 12 12 1 1
MucmuK 2x3+ 1 13 13 13 1 1
FunnyHouse 2x3+ 1 23 23 23 1 1
dedoo 2x3+ 1 7 7 7 1 1
iam1 2x3+ 1 18 18 18 1 1
RiSuS 2x3+ 1 7 7 7 1 1
cas 2x2 1 8 8 8 1 1
lama 2x2+ 1 4 4 4 1 1
dedoo 2x2+ 1 9 9 9 1 1
discourage 2x2 1 11 11 11 1 1
concretemixer 2x2 1 9 9 9 1 1
cas 2x3+ 4 24 6 7.0 1.5 1.5
mixei4 2x3+ 2 10 5 5.0 1.5 1.5
Enchante_ 2x2 1 3 3 3 2 2
lama 2x2 2 8 4 4.0 2 2.0
Daramant 2x3+ 1 1 1 1 2 2
Antmsu 2x2 1 4 4 4 2 2
Daramant 2x2+ 1 2 2 2 2 2
giperball 2x2+ 2 5 2.5 2.5 2 2.0
Lev 2x3+ 1 2 2 2 2 2
giperball 2x2 1 2 2 2 2 2
evil_homura 2x3+ 1 8 8 8 2 2
total 30 211 7.033333333333333 5.5 1.4666666666666666 1.0
opponent version game_type n total_score mean_score median_score mean_place median_place
robostac 127 2x3+ 1 16 16 16 1 1
ykaland 28 2x2+ 1 5 5 5 1 1
sergio-dna 33 2x2 1 12 12 12 1 1
MucmuK 17 2x3+ 1 13 13 13 1 1
FunnyHouse 37 2x3+ 1 23 23 23 1 1
dedoo 15 2x3+ 1 7 7 7 1 1
iam1 26 2x3+ 1 18 18 18 1 1
RiSuS 63 2x3+ 1 7 7 7 1 1
cas 26 2x2 1 8 8 8 1 1
lama 22 2x2+ 1 4 4 4 1 1
dedoo 15 2x2+ 1 9 9 9 1 1
discourage 54 2x2 1 11 11 11 1 1
concretemixer 51 2x2 1 9 9 9 1 1
cas 26 2x3+ 4 24 6 7.0 1.5 1.5
mixei4 27 2x3+ 2 10 5 5.0 1.5 1.5
Enchante_ 20 2x2 1 3 3 3 2 2
lama 22 2x2 2 8 4 4.0 2 2.0
Daramant 21 2x3+ 1 1 1 1 2 2
Antmsu 54 2x2 1 4 4 4 2 2
Daramant 21 2x2+ 1 2 2 2 2 2
giperball 88 2x2+ 2 5 2.5 2.5 2 2.0
Lev 23 2x3+ 1 2 2 2 2 2
giperball 88 2x2 1 2 2 2 2 2
evil_homura 29 2x3+ 1 8 8 8 2 2
total 30 211 7.033333333333333 5.5 1.4666666666666666 1.0
scripts/results_stats.py produces graphics and table report for multiple result.txt files from local runner launch.
Usage:
- Run local runner with
--results-fileoption multiple times using different file names. - Run report script:
scripts/results_stats.py result.*.txtExample:
scripts/results_stats.py log/v29_vs_a0ef50a/*
games 50
unique seeds 50
first second ratio (second/first)
_1 14 41 2.9285714285714284
_2 36 9 0.25
total_score 172 286 1.6627906976744187
min_score 1 2 2.0
max_score 8 12 1.5
mean_score 3.44 5.72 1.6627906976744184
median_score 3.0 5.0 1.6666666666666667
stdev_score 1.9708073555406327 2.4498229840258303 1.243055531094156
q95_score 7.0 10.0 1.4285714285714286
min_score_diff -10 -4 0.4
max_score_diff 4 10 2.5
mean_score_diff -2.28 2.28 -1.0
median_score_diff -2.0 2.0 -1.0
stdev_score_diff 3.2390474790327746 3.2390474790327746 1
1908789847 2128538004 432241528 730002022 283945802 630688912 2607963432 3714742159 1670516122 2647676947 2046736159 2974637113 508816090 2564744498 4089256807 1229238542 2004587353 561612246 901130959 4227012945 3231268869 3770445239 2186040580 3888576320 988935913 3600086806 3846219263 1597635489 1718150755 283133820 3029431657 2525454198 322098379 3588740731 4178094541 2288098860 1472899594 1486267384 1329421881 113597890 3674966996 4049527531 1451810575 1909916721 520566021 4223512919 1537191805 776895816 3568852997 2618877841
scripts/game_report.py produces graphics and table report for local runner game log.
Usage:
- Run local runner with
--log-fileoption. - Run report script:
scripts/game_report.py < game.logExample:
scripts/game_report.py < 389180.c1_scauto4z5zwmzazyszyjcvmpapxte.log
elsid-4 elsid-5 elsid-6 mixei4-1 mixei4-2 mixei4-3 elsid mixei4 ratio
mean speed 19.48112740336314 17.352230030805032 19.67599215415863 17.051426009221405 17.851066404799653 16.47375186164014 18.836449862775602 17.12541475855373 1.099912038823308
distance 90.96201222502278 80.9972738930335 91.85308225919583 79.28460161369888 83.1239941638729 76.76884941696686 263.8123683772521 239.17744519453862 1.1029985212973417
mean radius 1.004345369103563 1.00334304919398 1.0036791386592112 1.0013899232645291 1.0011974897388614 1.0002855273332936 1.0037891856522516 1.0009576467788948 1.0028288298535595
median radius 1.004345369103563 1.00334304919398 1.0036791386592112 1.0013899232645291 1.0011974897388614 1.0002855273332936 1.0037891856522516 1.0009576467788948 1.0028288298535595
hits - - - - - - 162 115 1.4086956521739131
initial ball.y ticks winner
5.949698463075475 598 mixei4
6.614135030758736 5082 elsid
6.890847866625197 624 mixei4
6.045366164172275 2078 elsid
2.554480156112114 617 elsid
4.76723537014998 1738 mixei4
4.9140376245386035 898 mixei4
7.7303627052045485 958 mixei4
2.4078679586678198 452 elsid
3.5857436392422053 2759 elsid
scripts/check_goalkeeper_report.py produces graphics and table report for simulation tool check goalkeeper result.
Usage:
- See simulation tool how to prepare data.
- Run report script:
scripts/check_goalkeeper_report.py < empty_and_goalkeeper.jsonExample:
scripts/check_goalkeeper_report.py < log/empty_and_goalkeeper.1549269720.1549916017.c64e615.10000.json
source simulations misses hits hits rate safes goals goals rate safes rate safes/goals
stdin 10000 8616 1384 0.1606313834726091 1170 214 0.1546242774566474 0.8453757225433526 5.4672897196261685
