test_single.sh

declare -a algo=('mrcql' 'cql')
declare -a experience=('random_episode')
declare -a sample_type=('none')
declare -a dataset=("halfcheetah-random-v0") 
declare -a dataset_nums=("500-0" "0-500")
declare -a critic_replay_type=("bc" "bc")
declare -a critic_replay_lambda=("100" "10")
declare -a actor_replay_type=("orl" "orl")
declare -a actor_replay_lambda=("1" "10")
declare -a max_save_num=('10000')
declare -a seed=('0')
for i in "${!dataset[@]}"
do
        for j in "${!dataset_nums[@]}"
        do
                for k in ${!algo[@]}
                do
                        for l in "${!critic_replay_type[@]}"
                        do
                                for m in "${!max_save_num[@]}"
                                do
                                        echo "
                                        python continual_single.py --algo ${algo[$k]} --experience_type random_episode --dataset ${dataset[$i]} --dataset_nums ${dataset_nums[$j]} --max_save_num ${max_save_num[$m]} --critic_replay_type ${critic_replay_type[$l]} --critic_replay_lambda ${critic_replay_lambda[$l]} --actor_replay_type ${actor_replay_type[$l]} --actor_replay_lambda ${actor_replay_lambda[$l]} --read_policies 0 --seed ${seed[$s]} --test > single_output_files/test_${algo[$k]}_${dataset[$i]}_${dataset_nums[$j]}_${critic_replay_type[$l]}_${critic_replay_lambda[$l]}_${actor_replay_type[$l]}_${actor_replay_lambda[$l]}_${max_save_num[$m]}_${seed[$s]}_\$(date +%Y%m%d%H%M%S).txt --gpu \$1" > single_run_files/test_${algo[$k]}_${dataset[$i]}_${dataset_nums[$j]}_${critic_replay_type[$l]}_${critic_replay_lambda[$l]}_${actor_replay_type[$l]}_${actor_replay_lambda[$l]}_${max_save_num[$m]}_${seed[$s]}.sh
                                done
                        done
                done
        done
done