Live Breaking News & Updates on Simulation Environment|Page 2

Stay updated with breaking news from Simulation environment. Get real-time updates on events, politics, business, and more. Visit us for reliable news and exclusive interviews.

AAEON and Cogniteam's Partnership Prove Modern Robotics is All About Integrations

AAEON, a leading designer and manufacturer of industrial IoT and Edge AI solutions, has partnered with Cogniteam to develop ready-to-build robotic hardware that leverages Cogniteam’s Nimbus software, the leading drag and drop robotics operating system. ....

Cogniteam Nimbus , Bowen Wei , Megan Craig , Yehuda Elmaliah , Digital Robot , Simulation Environment , Live Robot , Image Credit , Jetson Nano , Jetson Xavier ,

"Multi-agent deep reinforcement learning for traffic signal control wit" by Wei Wei, Qiang Wu et al.

Traffic signal control is an essential and chal-lenging real-world problem, which aims to alleviate traffic congestion by coordinating vehicles' movements at road in-tersections. Deep reinforcement learning (DRL) combines deep neural networks (DNNs) with a framework of reinforcement learning, which is a promising method for adaptive traffic signal control in complex urban traffic networks. Now, multi-agent deep reinforcement learning (MARL) has the potential to deal with traffic signal control at a large scale. However, current traffic signal control systems still rely heavily on simplified rule- based methods in practice. In this paper, we propose: (1) a MARL algorithm based on Nash Equilibrium and DRL, namely Nash Asynchronous Advantage Actor-Critic (Nash-A3C); (2) an urban simulation environment (SENV) to be essentially close to the real-world scenarios. We apply our method in SENV, obtaining better performance than benchmark traffic signal control methods by 22.1%, which prove ....

Gnash Equilibrium , Nash Asynchronous Advantage Actor Critic , Gnash Equilibrium , Ash A3c , Simulation Environment , Traffic Signal Control ,