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Code for Action abstractions for amortized sampling, published at ICLR 2025

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chunk-gfn

Installation

This project requires python>=3.10. To install, we recommend first setting up a virtual environment of your choice, and then pip installing this package:

pip install -e .

Running Experiments

Experiment runs can be found in sbatch_scripts/. Runs are run via main.py and all options are handled by hydra. See below for an example.

python main.py seed=42 data=bit_sequence gfn=tb_gfn trainer.max_epochs=1000 data.max_len=128 gfn.replay_buffer.cutoff_distance=25 gfn.reward_temperature=0.3333 logger.wandb.name="prioritized-len-128"

Datasets

To make some datasets available, make sure to add this to your environment.

#!/bin/bash
export CHUNKGFN_DATA="/path/to/code/chunk-gfn/data"

to download those datasets, look in /path/to/code/chunk-gfn/data/${dataset}/download.sh.

Logs

The logging directory is determined in configs/paths/default.yaml it is by default log_dir: ${oc.env:PROJECT_DIR}/logs/ and could be changed if to any location in your environment if desired.

When using SLURM, the system will automatically define the following environment variables and our code expects them to be defined. When not using slurm, SLURM_JOB_ID and SLURM_JOB_NAME will be automatically generated. This will determine the log directory.

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Code for Action abstractions for amortized sampling, published at ICLR 2025

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