no code implementations • 30 Jul 2023 • Sihong He, Shuo Han, Fei Miao
In this work, we design a multi-agent reinforcement learning (MARL)-based framework for EAVs balancing in E-AMoD systems, with adversarial agents to model both the EAVs supply and mobility demand uncertainties that may undermine the vehicle balancing solutions.
1 code implementation • 30 Jul 2023 • Sihong He, Songyang Han, Sanbao Su, Shuo Han, Shaofeng Zou, Fei Miao
Then we propose a robust multi-agent Q-learning (RMAQ) algorithm to find such an equilibrium, with convergence guarantees.
1 code implementation • 6 Dec 2022 • Songyang Han, Sanbao Su, Sihong He, Shuo Han, Haizhao Yang, Shaofeng Zou, Fei Miao
Various methods for Multi-Agent Reinforcement Learning (MARL) have been developed with the assumption that agents' policies are based on accurate state information.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 17 Sep 2022 • Sihong He, Yue Wang, Shuo Han, Shaofeng Zou, Fei Miao
In this work, we design a robust and constrained multi-agent reinforcement learning (MARL) framework with state transition kernel uncertainty for EV AMoD systems.
1 code implementation • 16 Sep 2022 • Sanbao Su, Yiming Li, Sihong He, Songyang Han, Chen Feng, Caiwen Ding, Fei Miao
Our work is the first to estimate the uncertainty of collaborative object detection.