This tutorial demonstrates technique to solve combinatorial optimization problems such as the well-known travelling salesman problem. The method was presented in the paper Neural Combinatorial Optimization with Reinforcement Learning.

The Algorithm applies the pointer network architecture wherein an attention mechanism is fashioned to point to elements of an input sequence, allowing a decoder to output said elements. The network is trained by reinforcement learning using an actor-critic method.

Note! This model does not beat existing baselines for TSP, moreover local search method LK-H solves these tsp tasks to optimality in seconds on a CPU, compared to suboptimal results by this model in several hours on a GPU.

The algorithm consists of two parts:

Intro to PN for simple sorting task: Intro to Pointer Network.ipynb.

Paper: Pointer Networks.

Blog post by fast ml: Introduction to pointer networks.

Neural Combinatorial Optimization.ipynb

Paper: Neural Combinatorial Optimization with Reinforcement Learning