A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing
  We propose a Deep Reinforcement Learning (Deep RL) algorithm for solving the
online 3D bin packing problem for an arbitrary number of bins and any bin size.
The focus is on producing decisions that can be physically implemented by a
robotic loading arm, a laboratory prototype used for testing the concept. The
problem considered in this paper is novel in two ways. First, unlike the
traditional 3D bin packing problem, we assume that the entire set of objects to
be packed is not known a priori. Instead, a fixed number of upcoming objects is
visible to the loading system, and they must be loaded in the order of arrival.
Second, the goal is not to move objects from one point to another via a
feasible path, but to find a location and orientation for each object that
maximises the overall packing efficiency of the bin(s). Finally, the learnt
model is designed to work with problem instances of arbitrary size without
retraining. Simulation results show that the RL-based method outperforms
state-of-the-art online bin packing heuristics in terms of empirical
competitive ratio and volume efficiency.
