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A2C playing AntBulletEnv-v0 from https://github.com/sgoodfriend/rl-algo-impls/tree/2067e21d62fff5db60168687e7d9e89019a8bfc0
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while getopts a:e:j:n:s:i: flag
do
case "${flag}" in
a) algo=${OPTARG};;
e) env=${OPTARG};;
j) n_jobs=${OPTARG};;
n) study_name=${OPTARG};;
s) seeds=${OPTARG};;
i) increment=${OPTARG};;
esac
done
TZ="America/Los_Angeles"
NOW=$(date +"%Y-%m-%dT%H:%M:%S")
study_name="${study_name:-$algo-$env-$NOW}"
STORAGE_PATH="sqlite:///runs/tuning.db"
increment="${increment:-100}"
mkdir -p runs
optuna create-study --study-name $study_name --storage $STORAGE_PATH --direction maximize --skip-if-exists
optimize () {
for ((j=$increment;j<=n_jobs*100+$increment;j+=100)); do
seed=()
for ((s=0;s<seeds;s++)); do
seed+="$((j+s*100/seeds)) "
done
echo python optimize.py --algo $algo --env $env --seed $seed --load-study --study-name $study_name --storage-path $STORAGE_PATH
done
}
optimize | xargs -I CMD -P $n_jobs bash -c CMD