objective-robustness-failures

Reproducing experiments

To reproduce the experiments, first navigate to the "train-procgen-pytorch" folder:

cd train-procgen-pytorch

Coinrun

Train:

python train.py --exp_name coinrun --env_name coinrun --num_levels 100000 --distribution_mode hard --param_name hard-500 --num_timesteps 200000000 --num_checkpoints 5 --seed 6033 --random_percent 0

In order to reproduce the experiments from the ablation, change the random_percent variable.

Test:

python render.py --exp_name coinrun_test --env_name coinrun_aisc --distribution_mode hard --param_name hard-500 --model_file PATH_TO_MODEL_FILE

where PATH_TO_MODEL_FILE is the path to the model file generated by the above training command.

Maze (Variant 1)

python train.py --exp_name maze1 --env_name maze_aisc --num_levels 100000 --distribution_mode hard --param_name hard-500 --num_timesteps 200000000 --num_checkpoints 5 --seed 1080

python render.py --exp_name maze1_test --env_name maze --distribution_mode hard --param_name hard-500  --model_file PATH_TO_MODEL_FILE

Maze (Variant 2)

python train.py --exp_name maze2 --env_name maze_yellowgem --num_levels 100000 --distribution_mode hard --param_name hard-500 --num_timesteps 200000000 --num_checkpoints 5 --seed 2809

python render.py --exp_name maze2_test --env_name maze_redgem_yellowstar --distribution_mode hard --param_name hard-500  --model_file PATH_TO_MODEL_FILE

Keys and Chests

python train.py --exp_name keys_chests --env_name heist_aisc_many_chests --num_levels 100000 --distribution_mode hard --param_name hard-500 --num_timesteps 200000000 --num_checkpoints 5 --seed 1111

python render.py --exp_name maze2_test --env_name heist_aisc_many_keys --distribution_mode hard --param_name hard-500  --model_file PATH_TO_MODEL_FILE