mlsanityData to 14 Oct 2026

SortingEnv: An Extendable RL-Environment for an Industrial Sorting Process

arXiv 2503.10466 ↗First posted 13 Mar 2025cs.LGcited by 3

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

Abstract (arXiv)

We present a novel reinforcement learning (RL) environment designed to both optimize industrial sorting systems and study agent behavior in evolving spaces. In simulating material flow within a sorting process our environment follows the idea of a digital twin, with operational parameters like belt speed and occupancy level. To reflect real-world challenges, we integrate common upgrades to industrial setups, like new sensors or advanced machinery. It thus includes two variants: a basic version focusing on discrete belt speed adjustments and an advanced version introducing multiple sorting modes and enhanced material composition observations. We detail the observation spaces, state update mechanisms, and reward functions for both environments. We further evaluate the efficiency of common RL algorithms like Proximal Policy Optimization (PPO), Deep-Q-Networks (DQN), and Advantage Actor Critic (A2C) in comparison to a classical rule-based agent (RBA). This framework not only aids in optimizing industrial processes but also provides a foundation for studying agent behavior and transferability in evolving environments, offering insights into model performance and practical implications for real-world RL applications.

Key facts. Cited by 3 papers in the map (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 6 of its 22 references that are in the map. First posted on arXiv 13 Mar 2025.

Builds on

6 of 22 references.

References · 6

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  4. 2020Transfer Learning in Deep Reinforcement Learning: A Surveycited by 265
  5. 2017A Benchmark Environment Motivated by Industrial Control Problemscited by 31
  6. 2023ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocationcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Leveraging Genetic Algorithms for Efficient Demonstration Generation in Real-World Reinforcement Learning Environmentscited by 3
  2. 2025Balancing Specialization and Centralization: A Multi-Agent Reinforcement Learning Benchmark for Sequential Industrial Controlcited by 0
  3. 2026Evolutionary Warm-Starts for Reinforcement Learning in Industrial Continuous Controlcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Reinforcement Learning to improve delta robot throws for sorting scrap metal
  2. 2024Solving a Real-World Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineering
  3. 2024An Efficient Multi-Robot Arm Coordination Strategy for Pick-and-Place Tasks using Reinforcement Learning
  4. 2025Curriculum RL meets Monte Carlo Planning: Optimization of a Real World Container Management Problem
  5. 2020The reinforcement learning-based multi-agent cooperative approach for the adaptive speed regulation on a metallurgical pickling line ↗
  6. 2024Deep Reinforcement Learning for Dynamic Order Picking in Warehouse Operations
  7. 2026Learning to Staff: Offline Reinforcement Learning and Fine-Tuned LLMs for Warehouse Staffing Optimization
  8. 2026Learning-guided Prioritized Planning for Lifelong Multi-Agent Path Finding in Warehouse Automation