# Mug

EXP="r1"   CUDA_VISIBLE_DEVICES=0, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 0 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --object_type mug --savename grasping_0.003 &
EXP="r0"   CUDA_VISIBLE_DEVICES=0, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 0 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --disable_gradients --object_type mug --savename grasping_0.003 &
EXP="l2c"  CUDA_VISIBLE_DEVICES=0, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 0 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --reinforce --no-temper --repair --predict --object_type mug --savename grasping_0.003 &
EXP="gd"   CUDA_VISIBLE_DEVICES=0, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 0 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --disable_stochasticity --repair --predict --no-temper --object_type mug --savename grasping_0.003 &

EXP="r1"   CUDA_VISIBLE_DEVICES=1, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 1 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --object_type mug --savename grasping_0.003 &
EXP="r0"   CUDA_VISIBLE_DEVICES=1, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 1 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --disable_gradients --object_type mug --savename grasping_0.003 &
EXP="l2c"  CUDA_VISIBLE_DEVICES=1, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 1 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --reinforce --no-temper --repair --predict --object_type mug --savename grasping_0.003 &
EXP="gd"   CUDA_VISIBLE_DEVICES=1, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 1 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --disable_stochasticity --repair --predict --no-temper --object_type mug --savename grasping_0.003 &

EXP="r1"   CUDA_VISIBLE_DEVICES=2, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 2 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --object_type mug --savename grasping_0.003 &
EXP="r0"   CUDA_VISIBLE_DEVICES=2, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 2 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --disable_gradients --object_type mug --savename grasping_0.003 &
EXP="l2c"  CUDA_VISIBLE_DEVICES=2, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 2 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --reinforce --no-temper --repair --predict --object_type mug --savename grasping_0.003 &
EXP="gd"   CUDA_VISIBLE_DEVICES=2, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 2 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --disable_stochasticity --repair --predict --no-temper --object_type mug --savename grasping_0.003 &

EXP="r1"   CUDA_VISIBLE_DEVICES=3, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 3 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --object_type mug --savename grasping_0.003 &
EXP="r0"   CUDA_VISIBLE_DEVICES=3, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 3 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --disable_gradients --object_type mug --savename grasping_0.003 &
EXP="l2c"  CUDA_VISIBLE_DEVICES=3, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 3 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --reinforce --no-temper --repair --predict --object_type mug --savename grasping_0.003 &
EXP="gd"   CUDA_VISIBLE_DEVICES=3, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 3 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --disable_stochasticity --repair --predict --no-temper --object_type mug --savename grasping_0.003 &

# box
wait;

EXP="r1"   CUDA_VISIBLE_DEVICES=0, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 0 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --object_type box --savename grasping_0.003 &
EXP="r0"   CUDA_VISIBLE_DEVICES=0, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 0 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --disable_gradients --object_type box --savename grasping_0.003 &
EXP="l2c"  CUDA_VISIBLE_DEVICES=0, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 0 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --reinforce --no-temper --repair --predict --object_type box --savename grasping_0.003 &
EXP="gd"   CUDA_VISIBLE_DEVICES=0, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 0 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --disable_stochasticity --repair --predict --no-temper --object_type box --savename grasping_0.003 &

EXP="r1"   CUDA_VISIBLE_DEVICES=1, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 1 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --object_type box --savename grasping_0.003 &
EXP="r0"   CUDA_VISIBLE_DEVICES=1, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 1 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --disable_gradients --object_type box --savename grasping_0.003 &
EXP="l2c"  CUDA_VISIBLE_DEVICES=1, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 1 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --reinforce --no-temper --repair --predict --object_type box --savename grasping_0.003 &
EXP="gd"   CUDA_VISIBLE_DEVICES=1, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 1 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --disable_stochasticity --repair --predict --no-temper --object_type box --savename grasping_0.003 &

EXP="r1"   CUDA_VISIBLE_DEVICES=2, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 2 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --object_type box --savename grasping_0.003 &
EXP="r0"   CUDA_VISIBLE_DEVICES=2, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 2 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --disable_gradients --object_type box --savename grasping_0.003 &
EXP="l2c"  CUDA_VISIBLE_DEVICES=2, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 2 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --reinforce --no-temper --repair --predict --object_type box --savename grasping_0.003 &
EXP="gd"   CUDA_VISIBLE_DEVICES=2, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 2 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --disable_stochasticity --repair --predict --no-temper --object_type box --savename grasping_0.003 &

EXP="r1"   CUDA_VISIBLE_DEVICES=3, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 3 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --object_type box --savename grasping_0.003 &
EXP="r0"   CUDA_VISIBLE_DEVICES=3, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 3 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --disable_gradients --object_type box --savename grasping_0.003 &
EXP="l2c"  CUDA_VISIBLE_DEVICES=3, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 3 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --reinforce --no-temper --repair --predict --object_type box --savename grasping_0.003 &
EXP="gd"   CUDA_VISIBLE_DEVICES=3, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 3 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --disable_stochasticity --repair --predict --no-temper --object_type box --savename grasping_0.003 &

# bowl
wait;

EXP="r1"   CUDA_VISIBLE_DEVICES=0, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 0 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --object_type bowl --savename grasping_0.003 &
EXP="r0"   CUDA_VISIBLE_DEVICES=0, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 0 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --disable_gradients --object_type bowl --savename grasping_0.003 &
EXP="l2c"  CUDA_VISIBLE_DEVICES=0, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 0 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --reinforce --no-temper --repair --predict --object_type bowl --savename grasping_0.003 &
EXP="gd"   CUDA_VISIBLE_DEVICES=0, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 0 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --disable_stochasticity --repair --predict --no-temper --object_type bowl --savename grasping_0.003 &

EXP="r1"   CUDA_VISIBLE_DEVICES=1, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 1 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --object_type bowl --savename grasping_0.003 &
EXP="r0"   CUDA_VISIBLE_DEVICES=1, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 1 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --disable_gradients --object_type bowl --savename grasping_0.003 &
EXP="l2c"  CUDA_VISIBLE_DEVICES=1, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 1 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --reinforce --no-temper --repair --predict --object_type bowl --savename grasping_0.003 &
EXP="gd"   CUDA_VISIBLE_DEVICES=1, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 1 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --disable_stochasticity --repair --predict --no-temper --object_type bowl --savename grasping_0.003 &

EXP="r1"   CUDA_VISIBLE_DEVICES=2, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 2 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --object_type bowl --savename grasping_0.003 &
EXP="r0"   CUDA_VISIBLE_DEVICES=2, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 2 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --disable_gradients --object_type bowl --savename grasping_0.003 &
EXP="l2c"  CUDA_VISIBLE_DEVICES=2, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 2 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --reinforce --no-temper --repair --predict --object_type bowl --savename grasping_0.003 &
EXP="gd"   CUDA_VISIBLE_DEVICES=2, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 2 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --disable_stochasticity --repair --predict --no-temper --object_type bowl --savename grasping_0.003 &

EXP="r1"   CUDA_VISIBLE_DEVICES=3, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 3 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --object_type bowl --savename grasping_0.003 &
EXP="r0"   CUDA_VISIBLE_DEVICES=3, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 3 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --L 10 --repair --predict --disable_gradients --object_type bowl --savename grasping_0.003 &
EXP="l2c"  CUDA_VISIBLE_DEVICES=3, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 3 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --reinforce --no-temper --repair --predict --object_type bowl --savename grasping_0.003 &
EXP="gd"   CUDA_VISIBLE_DEVICES=3, XLA_PYTHON_CLIENT_MEM_FRACTION=0.2 python architect/experiments/simple_grasping/predict_and_mitigate.py --model_path results/submission/grasping_mug/initial_policy.eqx --seed 3 --num_rounds 5 --num_steps_per_round 10 --num_chains 5 --ep_mcmc_step_size 1e-3 --dp_mcmc_step_size 1e-5 --disable_stochasticity --repair --predict --no-temper --object_type bowl --savename grasping_0.003 &
