  Generative Flow Networks (GFlowNets) are a family of probabilistic generative
models that learn to sample compositional objects proportional to their
rewards. One big challenge of GFlowNets is training them effectively when
dealing with long time horizons and sparse rewards. To address this, we propose
Evolution guided generative flow networks (EGFN), a simple but powerful
augmentation to the GFlowNets training using Evolutionary algorithms (EA). Our
method can work on top of any GFlowNets training objective, by training a set
of agent parameters using EA, storing the resulting trajectories in the
prioritized replay buffer, and training the GFlowNets agent using the stored
trajectories. We present a thorough investigation over a wide range of toy and
real-world benchmark tasks showing the effectiveness of our method in handling
long trajectories and sparse rewards.
