PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimization
This addresses the challenge of efficient solution space exploration for combinatorial optimization, offering an incremental improvement over existing learning-based methods.
The paper tackles the problem of limited exploration in reinforcement learning-based combinatorial optimization by introducing PolyNet, which learns complementary solution strategies without handcrafted diversity rules, resulting in better solutions than explicit diversity-enforcing approaches across four problems.
Reinforcement learning-based methods for constructing solutions to combinatorial optimization problems are rapidly approaching the performance of human-designed algorithms. To further narrow the gap, learning-based approaches must efficiently explore the solution space during the search process. Recent approaches artificially increase exploration by enforcing diverse solution generation through handcrafted rules, however, these rules can impair solution quality and are difficult to design for more complex problems. In this paper, we introduce PolyNet, an approach for improving exploration of the solution space by learning complementary solution strategies. In contrast to other works, PolyNet uses only a single-decoder and a training schema that does not enforce diverse solution generation through handcrafted rules. We evaluate PolyNet on four combinatorial optimization problems and observe that the implicit diversity mechanism allows PolyNet to find better solutions than approaches that explicitly enforce diverse solution generation.