mlsanityData to 14 Oct 2026

Optimizing 2D+1 Packing in Constrained Environments Using Deep Reinforcement Learning

arXiv 2503.17573 ↗First posted 21 Mar 2025cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

This paper proposes a novel approach based on deep reinforcement learning (DRL) for the 2D+1 packing problem with spatial constraints. This problem is an extension of the traditional 2D packing problem, incorporating an additional constraint on the height dimension. Therefore, a simulator using the OpenAI Gym framework has been developed to efficiently simulate the packing of rectangular pieces onto two boards with height constraints. Furthermore, the simulator supports multidiscrete actions, enabling the selection of a position on either board and the type of piece to place. Finally, two DRL-based methods (Proximal Policy Optimization -- PPO and the Advantage Actor-Critic -- A2C) have been employed to learn a packing strategy and demonstrate its performance compared to a well-known heuristic baseline (MaxRect-BL). In the experiments carried out, the PPO-based approach proved to be a good solution for solving complex packaging problems and highlighted its potential to optimize resource utilization in various industrial applications, such as the manufacturing of aerospace composites.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 36 references that are in the map. First posted on arXiv 21 Mar 2025.

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9 of 36 references.

References · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  3. 2021Heuristic-Guided Reinforcement Learningcited by 42
  4. 2021Learning Practically Feasible Policies for Online 3D Bin Packingcited by 18
  5. 2022A2C is a special case of PPOcited by 16
  6. 2021Self-play Learning Strategies for Resource Assignment in Open-RAN Networkscited by 8
  7. 2022Online 3D Bin Packing Reinforcement Learning Solution with Buffercited by 8
  8. 2021One model Packs Thousands of Items with Recurrent Conditional Query Learningcited by 5
  9. 2023Learning based 2D Irregular Shape Packingcited by 5

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  1. 2024Bin Packing Optimization via Deep Reinforcement Learning
  2. 2024Mitigating Dimensionality in 2D Rectangle Packing Problem under Reinforcement Learning Schema
  3. 2026Diffusion Reinforcement Learning Based Online 3D Bin Packing Spatial Strategy Optimization
  4. 2024An Efficient Deep Reinforcement Learning Model for Online 3D Bin Packing Combining Object Rearrangement and Stable Placement
  5. 2025Deliberate Planning of 3D Bin Packing on Packing Configuration Trees
  6. 2026Deep Reinforcement Learning on Item-Compatibility Graphs for One-Dimensional Bin Packing
  7. 2025A Hierarchical Bin Packing Framework with Dual Manipulators via Heuristic Search and Deep Reinforcement Learning
  8. 2025One4Many-StablePacker: An Efficient Deep Reinforcement Learning Framework for the 3D Bin Packing Problem