no code implementations • 18 Aug 2023 • Arrasy Rahman, Jiaxun Cui, Peter Stone
In this work, we first propose that maximizing an AHT agent's robustness requires it to emulate policies in the minimum coverage set (MCS), the set of best-response policies to any partner policies in the environment.
1 code implementation • CVPR 2022 • Jiaxun Cui, Hang Qiu, Dian Chen, Peter Stone, Yuke Zhu
To evaluate our model, we develop AutoCastSim, a network-augmented driving simulation framework with example accident-prone scenarios.
1 code implementation • 3 Dec 2021 • Yulin Zhang, William Macke, Jiaxun Cui, Daniel Urieli, Peter Stone
This article establishes for the first time that a multiagent driving policy can be trained in such a way that it generalizes to different traffic flows, AV penetration, and road geometries, including on multi-lane roads.
1 code implementation • 26 Feb 2021 • Jiaxun Cui, William Macke, Harel Yedidsion, Daniel Urieli, Peter Stone
Next, we propose a modular transfer reinforcement learning approach, and use it to scale up a multiagent driving policy to outperform human-like traffic and existing approaches in a simulated realistic scenario, which is an order of magnitude larger than past scenarios (hundreds instead of tens of vehicles).