PyTorch implementation for our TMLR paper

Distributional GFlowNets with Quantile Flows.

Dinghuai Zhang*, Ling Pan*, Ricky T. Q. Chen, Aaron Courville, Yoshua Bengio.

We think of each edge flow as a random variable, and parameterize its quantile function in a distributional way. We then propose "Quantile Matching" (QM) to train the GFlowNet model based on a distributional temporal-difference-like 🤖 flow constraint. With such risk-sensitive probabilistic flows, GFlowNet now support risk-sensitive polices to deal with uncertainty in the reward models. To make things better, Quantile Matching even outperforms previous methods in non-stochastic environments🔬 due to richer learning signals.

python run_hydra.py ndim=4 method=fm
python run_hydra.py ndim=4 method=tb
python run_hydra.py ndim=4 method=qm N=8 quantile_dim=256

The last one is the proposed QM algorithm.

python gflownet.py obj=fm
python gflownet.py obj=tb reward_exp=4 random_action_prob=0.1
python gflownet.py obj=qm

The last one is the proposed QM algorithm.