9 citations · 19 across the 7 of their papers we have counts for
7 papers
Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers
Aleksandar Krnjaic, Raul D. Steleac, Jonathan D. Thomas +8
We consider 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, calle…
Federated Meta-Learning for Traffic Steering in O-RAN
Hakan Erdol, Xiaoyang Wang, Peizheng Li +7
The vision of 5G lies in providing high data rates, low latency (for the aim of near-real-time applications), significantly increased base station capacity, and near-perfect qualit…
Sim2real for Reinforcement Learning Driven Next Generation Networks
Peizheng Li, Jonathan Thomas, Xiaoyang Wang +8
The next generation of networks will actively embrace artificial intelligence (AI) and machine learning (ML) technologies for automation networks and optimal network operation stra…
Achieving Goals using Reward Shaping and Curriculum Learning
Mihai Anca, Jonathan D. Thomas, Dabal Pedamonti +2
Real-time control for robotics is a popular research area in the reinforcement learning community. Through the use of techniques such as reward shaping, researchers have managed to…
Multi-lingual agents through multi-headed neural networks
J. D. Thomas, R. Santos-Rodríguez, R. Piechocki +1
This paper considers cooperative Multi-Agent Reinforcement Learning, focusing on emergent communication in settings where multiple pairs of independent learners interact at varying…
RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN
Peizheng Li, Jonathan Thomas, Xiaoyang Wang +8
Radio access network (RAN) technologies continue to evolve, with Open RAN gaining the most recent momentum. In the O-RAN specifications, the RAN intelligent controllers (RICs) are…
Self-play Learning Strategies for Resource Assignment in Open-RAN Networks
Xiaoyang Wang, Jonathan D Thomas, Robert J Piechocki +3
Open Radio Access Network (ORAN) is being developed with an aim to democratise access and lower the cost of future mobile data networks, supporting network services with various Qo…