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"TransformerLight: A Novel Sequence Modeling Based Traffic Signaling Me" by Qiang Wu, Mingyuan Li et al.

Traffic signal control (TSC) is still one of the most significant and challenging research problems in the transportation field. Reinforcement learning (RL) has achieved great success in TSC but suffers from critically high learning costs in practical applications due to the excessive trial-and-error learning process. Offline RL is a promising method to reduce learning costs whereas the data distribution shift issue is still up in the air. To this end, in this paper, we formulate TSC as a sequence modeling problem with a sequence of Markov decision process described by states, actions, and rewards from the traffic environment. A novel framework, namely TransformerLight, is introduced, which does not aim to fit into value functions by averaging all possible returns, but produces the best possible actions using a gated Transformer. Additionally, the learning process of TransformerLight is much more stable by replacing the residual connections with gated transformer blocks due to a dynami ....

Dynamic Programming , Reinforcement Learning , Sequence Modeling , Traffic Signal Control ,

Companies Divided After Launch of ChatGPT Enterprise

ChatGPT Enterprise has divided opinions among companies. Explore how this new AI platform has impacted businesses, and why it's causing controversy. ....

New York , United States , Sarah Silverman , Barry Diller , New York Times , Reinforcement Learning , Human Feedback , Top Generative ,

Script Network Becomes NVIDIA Inception Premier Partner to Utilize AI in TV Streaming – Launching on Mantle This Fall

Script Network Becomes NVIDIA Inception Premier Partner to Utilize AI in TV Streaming – Launching on Mantle This Fall
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Script Network , Why Script Network Joining , Content Recommendation , Bitrate Streaming , Reinforcement Learning , Feed Analytics , Script Blockchain ,