Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2021

Abstract

Recently, there is growing attention on applying deep reinforcement learning (DRL) to solve the 3D bin packing problem (3D BPP), given its favorable generalization and independence of ground-truth label. However, due to the relatively less informative yet computationally heavy encoder, and considerably large action space inherent to the 3D BPP, existing methods are only able to handle up to 50 boxes. In this paper, we propose to alleviate this issue via an end-to-end multimodal DRL agent, which sequentially addresses three sub-tasks of sequence, orientation and position, respectively. The resulting architecture enables the agent to solve large-scale instances of 100 boxes or more. Experiments show that the agent could learn highly efficient policies that deliver superior performance against all the baselines on instances of various scales.

Keywords

Bin Packing Problem, Combinatorial Optimization Problem, Deep Reinforcement Learning, Multimodal Learning

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems, Virtual Event, United Kingdom, 2021 May 3-7

Volume

3

First Page

1548

Last Page

1550

ISBN

9781713832621

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

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