Meta-Learning-based Deep Reinforcement Learning for Multiobjective
  Optimization Problems
  Deep reinforcement learning (DRL) has recently shown its success in tackling
complex combinatorial optimization problems. When these problems are extended
to multiobjective ones, it becomes difficult for the existing DRL approaches to
flexibly and efficiently deal with multiple subproblems determined by weight
decomposition of objectives. This paper proposes a concise meta-learning-based
DRL approach. It first trains a meta-model by meta-learning. The meta-model is
fine-tuned with a few update steps to derive submodels for the corresponding
subproblems. The Pareto front is built accordingly. The computational
experiments on multiobjective traveling salesman problems demonstrate the
superiority of our method over most of learning-based and iteration-based
approaches.
