Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attention
arXiv:2107.04333
Abstract
This paper seeks to tackle the bin packing problem (BPP) through a learning perspective. Building on self-attention-based encoding and deep reinforcement learning algorithms, we propose a new end-to-end learning model for this task of interest. By decomposing the combinatorial action space, as well as utilizing a new training technique denoted as prioritized oversampling, which is a general scheme to speed up on-policy learning, we achieve state-of-the-art performance in a range of experimental settings. Moreover, although the proposed approach attend2pack targets offline-BPP, we strip our method down to the strict online-BPP setting where it is also able to achieve state-of-the-art performance. With a set of ablation studies as well as comparisons against a range of previous works, we hope to offer as a valid baseline approach to this field of study.
Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 38th International Conference on Machine Learning, 2021
References in corpus (7)
- Layer Normalization
- Counterfactual Multi-Agent Policy Gradients
- Attention, Learn to Solve Routing Problems!
- Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method
- A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem
- Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization
- Self-play Learning Strategies for Resource Assignment in Open-RAN Networks