Evolutionary RL for Container Loading

TL;DR

This paper introduces a hybrid evolutionary and reinforcement learning approach to optimize container loading sequences, outperforming heuristics and generalizing well to unseen problems in port operations.

Contribution

It presents a novel combination of Evolutionary Strategies and Policy Gradient RL to approximate optimal container loading sequences, improving over traditional heuristics.

Findings

RL agent learns near-optimal solutions

Outperforms heuristic solutions in experiments

Generalizes better with a solution pool

Abstract

Loading the containers on the ship from a yard, is an impor- tant part of port operations. Finding the optimal sequence for the loading of containers, is known to be computationally hard and is an example of combinatorial optimization, which leads to the application of simple heuristics in practice. In this paper, we propose an approach which uses a mix of Evolutionary Strategies and Reinforcement Learning (RL) tech- niques to find an approximation of the optimal solution. The RL based agent uses the Policy Gradient method, an evolutionary reward strategy and a Pool of good (not-optimal) solutions to find the approximation. We find that the RL agent learns near-optimal solutions that outperforms the heuristic solutions. We also observe that the RL agent assisted with a pool generalizes better for unseen problems than an RL agent without a pool. We present our results on synthetic data as well as on subsets of real-world problems taken from container terminal. The results validate that our approach does comparatively better than the heuristics solutions available, and adapts to unseen problems better.