  We study the problem of generating diverse candidates in the context of
Multi-Objective Optimization. In many applications of machine learning such as
drug discovery and material design, the goal is to generate candidates which
simultaneously optimize a set of potentially conflicting objectives. Moreover,
these objectives are often imperfect evaluations of some underlying property of
interest, making it important to generate diverse candidates to have multiple
options for expensive downstream evaluations. We propose Multi-Objective
GFlowNets (MOGFNs), a novel method for generating diverse Pareto optimal
solutions, based on GFlowNets. We introduce two variants of MOGFNs: MOGFN-PC,
which models a family of independent sub-problems defined by a scalarization
function, with reward-conditional GFlowNets, and MOGFN-AL, which solves a
sequence of sub-problems defined by an acquisition function in an active
learning loop. Our experiments on wide variety of synthetic and benchmark tasks
demonstrate advantages of the proposed methods in terms of the Pareto
performance and importantly, improved candidate diversity, which is the main
contribution of this work.
