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import sys | |
# Deliver more ore to hq (left side of the map) than your opponent. Use radars to find ore but beware of traps! | |
def std_err(args): | |
print(args, file=sys.stderr, flush=True) | |
def get_line(): | |
i = input() | |
std_err(i) | |
return i |
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module Basic01 | |
function main() | |
Threads.@threads for i = 1:10 | |
println("(threadid, loop_number) = ($(Threads.threadid()), $(i))") | |
end | |
end | |
end; Basic01.main() |
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import math | |
import matplotlib.pyplot as plt | |
import numpy as np | |
from scipy.stats import gamma, poisson | |
# parameters | |
E_p0 = 0.088 | |
g = 500 | |
a = E_p0 * g |
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party_id | name | ability | item | weapon1 | weapon2 | weapon3 | weapon4 | id | form | type1 | type2 | h | a | b | c | d | s | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | サザンドラ | ふゆう | こだわりスカーフ | あくのはどう | りゅうせいぐん | かえんほうしゃ | とんぼがえり | 1 | 1 | あく | ドラゴン | 92 | 105 | 90 | 125 | 90 | 98 | |
1 | ミミッキュ | ばけのかわ | いのちのたま | かげうち | シャドークロー | じゃれつく | つるぎのまい | 2 | 1 | ゴースト | フェアリー | 55 | 90 | 80 | 50 | 105 | 96 | |
1 | ドリュウズ | かたやぶり | きあいのタスキ | じしん | アイアンヘッド | がんせきふうじ | つのドリル | 3 | 1 | じめん | はがね | 110 | 135 | 60 | 50 | 65 | 88 | |
1 | ロトム | ふゆう | こだわりメガネ | ハイドロポンプ | 10まんボルト | ボルトチェンジ | トリック | 4 | 3 | でんき | みず | 50 | 65 | 107 | 105 | 107 | 86 | |
1 | カビゴン | あついしぼう | とつげきチョッキ | のしかかり | ヘビーボンバー | かみなりパンチ | じわれ | 5 | 1 | ノーマル | 160 | 110 | 65 | 65 | 110 | 30 | ||
1 | アーマーガア | ミラーアーマー | オボンのみ | ブレイブバード | ボディプレス | てっぺき | はねやすめ | 6 | 1 | ひこう | はがね | 98 | 87 | 105 | 53 | 85 | 67 | |
2 | ピカチュウ | せいでんき | でんきだま | ボルテッカー | じゃれつく | ねこだまし | かわらわり | 7 | 1 | でんき | 35 | 55 | 40 | 50 | 50 | 90 | ||
2 | バタフリー | ふくがん | きあいのタスキ | ぼうふう | ねむりごな | ギガドレイン | ちょうのまい | 8 | 1 | むし | ひこう | 60 | 45 | 50 | 90 | 80 | 70 | |
2 | カビゴン | くいしんぼう | フィラのみ | からげんき | じしん | ほのおのパンチ | はらだいこ | 9 | 1 | ノーマル | 160 | 110 | 65 | 65 | 110 | 30 |
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using Pkg; Pkg.add.(["ReinforcementLearning", "ReinforcementLearningEnvironments", "Flux", "StatsBase", "Plots"]) | |
# import Random # hide | |
# Random.seed!(1) # hide | |
using ReinforcementLearning, ReinforcementLearningEnvironments, Flux | |
# using StatsBase:mean | |
env = CartPoleEnv(;T=Float32) | |
ns, na = length(observation_space(env)), length(action_space(env)) # (4, 2) |