Feb 26, 2021 · In this paper, we scale up existing approaches and develop new multiagent driving policies for AVs in scenarios with greater complexity. We ...
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 ...
May 3, 2021 · In this paper, we scale up existing approaches and develop new multiagent driving policies for AVs in scenarios with greater complexity. We ...
Scalable Multiagent Driving Policies For Reducing Traffic Congestion (2021) ... Multiagent driving policies for AVs to reduce traffic congestion in more ...
This paper presents a meta-modelling system that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process ...
Apr 1, 2021 · Cui, J, Macke, W, Yedidsion, H, Goyal, A, Urieli, D, and Stone, P. "Scalable Multiagent Driving Policies For Reducing Traffic Congestion".
Flow is a computational framework for deep RL and control experiments for traffic microsimulation. See our website for more information.
People also ask
Which strategies could best assist in reducing traffic congestion?
Which methods for reducing traffic congestion should local councils use?
What are the benefits of reducing traffic congestion?
This paper presents a learned multiagent driving policy that is robust to a variety of open-network traffic conditions, including vehicle flows, the fraction of ...
May 10, 2022 · Scalable multiagent driving policies for reducing traffic congestion. arXiv preprint arXiv:2103.00058, 2021. [3] Liam Cummins, Yuchao Sun ...
Scalable multiagent driving policies for reducing traffic congestion. J Cui ... 2017. Learning a robust multiagent driving policy for traffic congestion reduction.