Dynamic Backtracking in GFlowNet: Enhancing Decision Steps with Reward-Dependent Adjustment Mechanisms

arXiv:2404.05576v1 Announce Type: new
Abstract: Generative Flow Networks (GFlowNets) are probabilistic models predicated on Markov flows, employing specific amortization algorithms to learn stochastic policies that generate compositional substances including biomolecules, chemical materials, and more. Demonstrating formidable prowess in generating high-performance biochemical molecules, GFlowNets accelerate the discovery of scientific substances, effectively circumventing the time-consuming, labor-intensive, and costly shortcomings intrinsic to conventional material discovery. However, previous work often struggles to accumulate exploratory experience and is prone to becoming disoriented within expansive sampling …

abstract algorithms arxiv backtracking cs.lg decision discovery dynamic flow generate generative learn markov materials molecules networks performance policies scientific stochastic type