1 citations · 1 across the 4 of their papers we have counts for
4 papers
Scalable Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantee: A Constrained Mean-Field Reinforcement Learning Approach
Matej Jusup, Kenan Zhang, Zhiyuan Hu +3
The expansion of ride-sourcing services such as Uber and Lyft has reshaped urban transportation by offering flexible, on-demand mobility via mobile applications. Despite convenienc…
Mean-Field Bayesian Optimisation
Petar Steinberg, Juliusz Ziomek, Matej Jusup +1
We address the problem of optimising the average payoff for a large number of cooperating agents, where the payoff function is unknown and treated as a black box. While standard Ba…
Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning
Matej Jusup, Barna Pásztor, Tadeusz Janik +4
Many applications, e.g., in shared mobility, require coordinating a large number of agents. Mean-field reinforcement learning addresses the resulting scalability challenge by optim…
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning
Volodymyr Tkachuk, Seyed Alireza Bakhtiari, Johannes Kirschner +3
A practical challenge in reinforcement learning are combinatorial action spaces that make planning computationally demanding. For example, in cooperative multi-agent reinforcement…