# rew
#!/bin/bash
#SBATCH --cpus-per-task=6
#SBATCH --tasks-per-node=1
#SBATCH --mem=16G
#SBATCH --nodes=1
#SBATCH --job-name=${tags.0}
#SBATCH --gres=gpu:p100:1
#SBATCH --signal=USR1@120
# #SBATCH --array=0-511%512
#SBATCH --time=00-3:00
#SBATCH --job-name=delay_aware
#SBATCH --output=./results/delay_aware.txt
#SBATCH --ntasks=1
# Uncomment below lines if required
# #SBATCH --partition=
# #SBATCH --qos=
# #SBATCH --comment=
# #SBATCH --constraint=
# #SBATCH --exclude=
# #SBATCH --cpus-per-gpu=
# #SBATCH --mem-per-gpu=
# #SBATCH --mem-per-cpu=
# Mail settings
#SBATCH --mail-user=${oc.env:EMAIL}
#SBATCH --mail-type=BEGIN,END,FAIL,ALL,REQUEUE
echo "Running a simple task"
python mbexp.py -logdir ./log/DATS \\
-env gym_pendulum \\
-o exp_cfg.exp_cfg.ntrain_iters 200 \\
-o exp_cfg.sim_cfg.delay_hor 10\\
-o ctrl_cfg.prop_cfg.delay_step 10\\
-ca opt-type CEM \\
-ca model-type PE \\
-ca prop-type E
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Codes for Paper "Delay-Aware Model-Based Reinforcement Learning for Continuous Control".
Waybaba/delay-aware-MBRL
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Codes for Paper "Delay-Aware Model-Based Reinforcement Learning for Continuous Control".
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