Many traditional algorithms for solving combinatorial optimization problems involve using
hand-crafted heuristics that sequentially construct a solution. Such heuristics are designed …

The vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem,
and many models and algorithms have been proposed to solve the VRP and its variants …

Deep reinforcement learning (RL) has shown impressive results in a variety of domains,
learning directly from high-dimensional sensory streams. However, when neural networks …

This paper proposes a learning-based approach to optimize the multiple traveling salesman
problem (MTSP), which is one classic representative of cooperative combinatorial …

Real-world planning problems often involve hundreds or even thousands of objects,
straining the limits of modern planners. In this work, we address this challenge by learning to …

It has been recently shown that general policies for many classical planning domains can be
expressed and learned in terms of a pool of features defined from the domain predicates …

We present the first approach capable of learning domain-independent planning heuristics
entirely from scratch. The heuristics we learn map the hypergraph representation of the …

While reinforcement learning methods have delivered remarkable results in a number of
settings, generalization, ie, the ability to produce policies that generalize in a reliable and …

We consider the problem of learning generalized policies for classical planning domains
using graph neural networks from small instances represented in lifted STRIPS. The …

With the increasing amount of available data, computing power and network speed for a
decreasing cost, the manufacturing industry is facing an unprecedented amount of data to …