mlsanityData to 14 Oct 2026

Container Unloading via Reinforcement Learning: Picking Order, Deadlock Avoidance, and Proof-of-Concept Simulation

arXiv 2605.27143 ↗First posted 26 May 2026eess.SY cs.SYcited by 0

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

Abstract (arXiv)

Unloading containers in the courier, express and parcel industry is a physically demanding and labor-intensive work. Automatizing this process is an important step towards increasing the efficiency of parcel-handling systems. This work investigates the potential of reinforcement learning to learn a policy for item selection in container unloading scenarios. For that, a simulation environment is created and a masked deep Q-learning with a specially designed neural network architecture is implemented. The results indicate that the agent can learn to select items with an average success rate of 60 %, which is significantly better than a random policy at a random chance of 20 %. The findings suggest that RL could be a promising approach for automatizing item unloading tasks in the future.

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 1 of its 8 references that are in the map. First posted on arXiv 26 May 2026.

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  1. 2017Deep Setscited by 1,421

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