Publications
For news about publications, follow us on X: 
Click on any author names or tags to filter publications.
All topic tags:
surveydeep-rlmulti-agent-rlagent-modellingad-hoc-teamworkautonomous-drivinggoal-recognitionexplainable-aicausalgeneralisationsecurityemergent-communicationiterated-learningintrinsic-rewardsimulatorstate-estimationdeep-learningtransfer-learning
Selected tags (click to remove):
Georgios-Papoudakissimulator
2024
Aleksandar Krnjaic, Raul D. Steleac, Jonathan D. Thomas, Georgios Papoudakis, Lukas Schäfer, Andrew Wing Keung To, Kuan-Ho Lao, Murat Cubuktepe, Matthew Haley, Peter Börsting, Stefano V. Albrecht
Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers
IEEE/RSJ International Conference on Intelligent Robots and Systems, 2024
Abstract | BibTex | arXiv | Website
IROSmulti-agent-rlsimulator
Abstract:
We envision a warehouse in which dozens of mobile robots and human pickers work together to collect and deliver items within the warehouse. The fundamental problem we tackle, called the order-picking problem, is how these worker agents must coordinate their movement and actions in the warehouse to maximise performance (e.g. order throughput). Established industry methods using heuristic approaches require large engineering efforts to optimise for innately variable warehouse configurations. In contrast, multi-agent reinforcement learning (MARL) can be flexibly applied to diverse warehouse configurations (e.g. size, layout, number/types of workers, item replenishment frequency), as the agents learn through experience how to optimally cooperate with one another. We develop hierarchical MARL algorithms in which a manager assigns goals to worker agents, and the policies of the manager and workers are co-trained toward maximising a global objective (e.g. pick rate). Our hierarchical algorithms achieve significant gains in sample efficiency and overall pick rates over baseline MARL algorithms in diverse warehouse configurations, and substantially outperform two established industry heuristics for order-picking systems
@inproceedings{krnjaic2024scalable,
title={Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers},
author={Aleksandar Krnjaic and Raul D. Steleac and Jonathan D. Thomas and Georgios Papoudakis and Lukas Sch\"afer and Andrew Wing Keung To and Kuan-Ho Lao and Murat Cubuktepe and Matthew Haley and Peter B\"orsting and Stefano V. Albrecht},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems},
year={2023}
}
2023
Aleksandar Krnjaic, Raul D. Steleac, Jonathan D. Thomas, Georgios Papoudakis, Lukas Schäfer, Andrew Wing Keung To, Kuan-Ho Lao, Murat Cubuktepe, Matthew Haley, Peter Börsting, Stefano V. Albrecht
Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers
arXiv:2212.11498, 2023
Abstract | BibTex | arXiv | Website
multi-agent-rlsimulator
Abstract:
We envision a warehouse in which dozens of mobile robots and human pickers work together to collect and deliver items within the warehouse. The fundamental problem we tackle, called the order-picking problem, is how these worker agents must coordinate their movement and actions in the warehouse to maximise performance (e.g. order throughput). Established industry methods using heuristic approaches require large engineering efforts to optimise for innately variable warehouse configurations. In contrast, multi-agent reinforcement learning (MARL) can be flexibly applied to diverse warehouse configurations (e.g. size, layout, number/types of workers, item replenishment frequency), as the agents learn through experience how to optimally cooperate with one another. We develop hierarchical MARL algorithms in which a manager assigns goals to worker agents, and the policies of the manager and workers are co-trained toward maximising a global objective (e.g. pick rate). Our hierarchical algorithms achieve significant gains in sample efficiency and overall pick rates over baseline MARL algorithms in diverse warehouse configurations, and substantially outperform two established industry heuristics for order-picking systems
@misc{krnjaic2023scalable,
title={Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers},
author={Aleksandar Krnjaic and Raul D. Steleac and Jonathan D. Thomas and Georgios Papoudakis and Lukas Sch\"afer and Andrew Wing Keung To and Kuan-Ho Lao and Murat Cubuktepe and Matthew Haley and Peter B\"orsting and Stefano V. Albrecht},
year={2023},
eprint={2212.11498},
archivePrefix={arXiv},
primaryClass={cs.LG}
}