Modern scene-graph task planners

pip install numpy==1.26.0
pip install protobuf==3.20.0
#pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118
#pip install torch-geometric==2.3.1
#pip install torch-scatter torch-sparse torch-cluster -f https://data.pyg.org/whl/torch-2.0.1+cu118.html

#pip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cu118
#pip install torch-scatter torch-sparse torch-cluster -f https://data.pyg.org/whl/torch-2.2.2+cu118.html

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
pip install torch-geometric==2.6.1
pip install torch-scatter torch-sparse torch-cluster -f https://data.pyg.org/whl/torch-2.6.0+cu126.html
pip install tensorboard
pip install torchviz==0.0.2
pip install pytorch-lightning==2.0.1
pip install wandb
pip install icecream
pip install pymimir==0.9.71
pip install pyperplan==2.1
pip install pandas
#for running baselines
pip install termcolor
pip install tarski
pip install clingo

For use with pddlgym, we require our fork of pddlgym, which houses our custom domains and problems.

Download and build the plan validation tool available at https://github.com/KCL-Planning/VAL, then make a symlink called validate on your path that points to the build/Validate binary, e.g. ln -s <path to VAL>/build/Validate /usr/local/bin/validate. If done successfully, running validate on your command line should give an output that starts with the line: VAL: The PDDL+ plan validation tool.

Running PLOI/SCRUB/SEEK on a registered pddlgym environment

To train a planner on an environment already registered with pddlgym, simply run main.py passing the appropriate commandline arguments.

For example, PLOI may be run on a domain Taskographyv2tiny10 by executing the following command. It trains on 40 problem instances for 401 epochs, and tests on all validation problem instances.

python main.py --domain taskographyv2tiny10 --method ploi --num-train-problems 40 --epochs 401 --mode train  --timeout 30 --expid taskographyv2tiny10_ploi --logdir cache/results --all-problems

To run evaluation using a pretrained model a PLOI baseline on the domain, set the --mode argument to test instead. The code will then pick up the best model from the directory pointed to by --expid.

Here's the list of supported commandline arguments across all planners.

usage: main.py [-h] [--seed SEED] [--method {scenegraph,hierarchical,ploi}]
               [--mode {train,test,visualize}] [--domain DOMAIN]
               [--train-planner-name {fd-lama-first,fd-opt-lmcut}]
               [--eval-planner-name {fd-lama-first,fd-opt-lmcut}]
               [--num-train-problems NUM_TRAIN_PROBLEMS]
               [--num-test-problems NUM_TEST_PROBLEMS]
               [--do-incremental-planning] [--timeout TIMEOUT] [--expid EXPID]
               [--logdir LOGDIR] [--device {cpu,cuda:0}] [--criterion {bce}]
               [--pos-weight POS_WEIGHT] [--epochs EPOCHS] [--lr LR]
               [--load-model] [--print-every PRINT_EVERY] [--gamma GAMMA]
               [--force-collect-data]

optional arguments:
  -h, --help            show this help message and exit
  --seed SEED           Random seed
  --method {scenegraph,hierarchical,ploi}
  --mode {train,test,visualize}
                        Mode to run the script in
  --domain DOMAIN       Name of the pddlgym domain to use.
  --train-planner-name {fd-lama-first,fd-opt-lmcut}
                        Train planner to use
  --eval-planner-name {fd-lama-first,fd-opt-lmcut}
                        Eval planner to use
  --num-train-problems NUM_TRAIN_PROBLEMS
                        Number of train problems
  --num-test-problems NUM_TEST_PROBLEMS
                        Number of test problems
  --do-incremental-planning
                        Whether or not to do incremental planning
  --timeout TIMEOUT     Timeout for test-time planner
  --expid EXPID         Unique exp id to log data to
  --logdir LOGDIR       Directory to store all expt logs in
  --device {cpu,cuda:0}
                        torch.device argument
  --criterion {bce}     Loss function to use
  --pos-weight POS_WEIGHT
                        Weight for the positive class in binary cross-entropy
                        computation
  --epochs EPOCHS       Number of epochs to run training for
  --lr LR               Learning rate
  --load-model          Path to load model from
  --print-every PRINT_EVERY
                        Number of iterations after which to print training
                        progress.
  --gamma GAMMA         Value of importance threshold (gamma) for PLOI.
  --force-collect-data  Force data collection (ignore pre-cached datasets).