JAIR paper source code & dataset & weights & results

Release v5.0.0 contains a large refactoring on the experiment scripts (command line interface) as well as the module organization.

• Bunch of doman-specific launcher code is moved to latplan/main/[domain].py , and they now have argparse-based docstring that describe the usage.
• Various mixins are moved to latplan/mixins/*.py .
• Output activations, loss functions and output visualization/rendering rules are abstracted into one class. For example, an unactivated output + L2 loss is named GaussianOutput, and sigmoid output + binary cross entropy loss is named ProbabilityOutput. The result partially resembles probabilistic programming.

The attached binaries are:

• cylinders-4-dataset-raw.tar.bz2 contains raw images generated by https://github.com/IBM/photorealistic-blocksworld/tree/jair , from which more compact dataset archives are created.
• datasets.tar contains .npz files storing the dataset.
• backup-propositional.tar.bz2 contains problem instances for planning.
• tables.tar.bz2 contains csv files containing various summary statistics of trained models as well as planning results. The files expands into tables/ directory which already exists in the repository. Running make will generate plots, and also a sqlite database file as a byproduct that makes it easy to investigate the results. (use utilities such as sqlitebrowser)
• samples-*.bz2 contains trained weights for AMA4, kltune (prior = bernoulli(0.1) ) configurations. Due to the file size, we included the top-5 ELBO configurations only.