Evolutionary RL for Container Loading
  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.
