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Generative Flow Networks as Entropy-Regularized RL

Official code for the paper Generative Flow Networks as Entropy-Regularized RL.

Daniil Tiapkin*, Nikita Morozov*, Alexey Naumov, Dmitry Vetrov.

Installation

• Create conda environment:

conda create -n gflownet-rl python=3.10
conda activate gflownet-rl

• Install PyTorch with CUDA. For our experiments we used the following versions:

conda install pytorch==2.0.0 torchvision==0.15.0 pytorch-cuda=11.8 -c pytorch -c nvidia

You can change pytorch-cuda=11.8 with pytorch-cuda=XX.X to match your version of CUDA.

• Install core dependencies:

pip install -r requirements.txt

-(Optional) Install dependencies for molecule experiemtns

pip install -r requirements_mols.txt

You can change requirements_mols.txt to match your CUDA version by replacing cu118 by cuXXX.

Hypergrids

Code for this part heavily utlizes library torchgfn (https://github.com/GFNOrg/torchgfn).

Path to configurations (utlizes ml-collections library):

• General configuration: hypergrid/experiments/config/general.py
• Algorithm: hypergrid/experiments/config/algo.py
• Environment: hypergrid/experiments/config/hypergrid.py

List of available algorithms:

• Baselines: db, tb, subtb from torchgfn library;
• Soft RL algorithms: soft_dqn, munchausen_dqn, sac.

Example of running the experiment on environment with height=20, ndim=4 with standard rewards, seed 3 on the algorithm soft_dqn.

    python run_hypergrid_exp.py --general experiments/config/general.py:3 --env experiments/config/hypergrid.py:standard --algo experiments/config/algo.py:soft_dqn --env.height 20 --env.ndim 4

To activate learnable backward policy for this setting

    python run_hypergrid_exp.py --general experiments/config/general.py:3 --env experiments/config/hypergrid.py:standard --algo experiments/config/algo.py:soft_dqn --env.height 20 --env.ndim 4 --algo.tied True --algo.uniform_pb False

Molecules

The presented experiments actively reuse the existing codebase for molecule generation experiments with GFlowNets (https://github.com/GFNOrg/gflownet/tree/subtb/mols).

Additional requirements for molecule experiments:

• pandas rdkit torch_geometric h5py ray hydra (installation is available in requirements_mols.txt)

Path to configurations of MunchausenDQN (utilizes hydra library)

• General configuration: mols/configs/soft_dqn.yaml
• Algorithm: mols/configs/algorithm/soft_dqn.yaml
• Environment: mols/configs/environment/block_mol.yaml

To run MunchausenDQN with configurations prescribed above, use

    python soft_dqn.py

To reporoduce baselines, run gflownet.py with required parameters, we refer to the original repository https://github.com/GFNOrg/gflownet for additional details.

Bit sequences

Examples of running TB, DB and SubTB baselines for word length k=8:

python bitseq/run.py --objective tb --k 8 --learning_rate 0.002

python bitseq/run.py --objective db --k 8 --learning_rate 0.002

python bitseq/run.py --objective subtb --k 8 --learning_rate 0.002 --subtb_lambda 1.9

Example of running SoftDQN:

python bitseq/run.py --objective softdqn --m_alpha 0.0 --k 8 --learning_rate 0.002 --leaf_coeff 2.0 

Example of running MunchausenDQN:

python bitseq/run.py --objective softdqn --m_alpha 0.15 --k 8 --learning_rate 0.002 --leaf_coeff 2.0 

Citation

@inproceedings{tiapkin2024generative,
  title={Generative flow networks as entropy-regularized rl},
  author={Tiapkin, Daniil and Morozov, Nikita and Naumov, Alexey and Vetrov, Dmitry P},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={4213--4221},
  year={2024},
  organization={PMLR}
}