Attention-based Policy Gradient RL for learning to solve Travelling Salesperson Problem

A PyTorch implementation of the attention-based Policy Gradient RL for learning to solve Travelling Salesperson Problem based on the paper https://doi.org/10.1007/978-3-319-93031-2_12

This project is the PyTorch implementation of the attention-based policy gradient for solving TSP based on the paper Learning Heuristics for the TSP by Policy Gradient [PDF here]

The paper also incorporates a 2-opt heuristic to the RL algorithm, which we did not include here. Please refer to the paper for a more detailed explanation of the methodology.

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Architecture

Neural (transformer) Encoder of the Policy Net

Neural (pointer) Decoder of the Policy Net

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Training

The agent is trained using the REINFORCE (policy gradient) algorithm. refer to the REINFORCE original paper.