# RTAW: An Attention Inspired Reinforcement Learning Method for Multi-Robot Task Allocation in Warehouse Environments

arXiv: https://arxiv.org/abs/2209.05738 · HTML: https://mlsanity.com/paper/2209.05738/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2022.

## Built on by (most cited first, 12 of 12)

- [RoboOS: A Hierarchical Embodied Framework for Cross-Embodiment and Multi-Agent Collaboration](https://mlsanity.com/paper/2505.03673/) (2025)
- [MAGNNET: Multi-Agent Graph Neural Network-based Efficient Task Allocation for Autonomous Vehicles with Deep Reinforcement Learning](https://mlsanity.com/paper/2502.02311/) (2025)
- [Robustness to Multi-Modal Environment Uncertainty in MARL using Curriculum Learning](https://mlsanity.com/paper/2310.08746/) (2023)
- [Deep Reinforcement Learning for Decentralized Multi-Robot Control: A DQN Approach to Robustness and Information Integration](https://mlsanity.com/paper/2408.11339/) (2024)
- [HARP: Human-Assisted Regrouping with Permutation Invariant Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2409.11741/) (2024)
- [Together We Rise: Optimizing Real-Time Multi-Robot Task Allocation using Coordinated Heterogeneous Plays](https://mlsanity.com/paper/2502.16079/) (2025)
- [A Two-stage Reinforcement Learning-based Approach for Multi-entity Task Allocation](https://mlsanity.com/paper/2407.00496/) (2024)
- [State-of-the-art in Robot Learning for Multi-Robot Collaboration: A Comprehensive Survey](https://mlsanity.com/paper/2408.11822/) (2024)
- [GRAND: Guidance, Rebalancing, and Assignment for Networked Dispatch in Multi-Agent Path Finding](https://mlsanity.com/paper/2512.03194/) (2025)
- [Coordination Matters: Evaluation of Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2605.06557/) (2026)
- [Integrated Optimization of Automated Warehouse Operations and Last-Mile Transport for Differentiated On-Demand Delivery](https://mlsanity.com/paper/2609.19048/) (2026)
- [Reinforcement Learning Based Heterogeneous Task Allocation Under Spatio-Temporal Environmental Uncertainty for Multi-Robot Teams](https://mlsanity.com/paper/2610.15237/) (2026)

## Abstract (arXiv)

We present a novel reinforcement learning based algorithm for multi-robot task allocation problem in warehouse environments. We formulate it as a Markov Decision Process and solve via a novel deep multi-agent reinforcement learning method (called RTAW) with attention inspired policy architecture. Hence, our proposed policy network uses global embeddings that are independent of the number of robots/tasks. We utilize proximal policy optimization algorithm for training and use a carefully designed reward to obtain a converged policy. The converged policy ensures cooperation among different robots to minimize total travel delay (TTD) which ultimately improves the makespan for a sufficiently large task-list. In our extensive experiments, we compare the performance of our RTAW algorithm to state of the art methods such as myopic pickup distance minimization (greedy) and regret based baselines on different navigation schemes. We show an improvement of upto 14% (25-1000 seconds) in TTD on scenarios with hundreds or thousands of tasks for different challenging warehouse layouts and task generation schemes. We also demonstrate the scalability of our approach by showing performance with up to 1000 robots in simulations.