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Found 3 papers, 2 papers with code
Delving into Macro Placement with Reinforcement Learning
In physical design, human designers typically place macros via trial and error, which is a Markov decision process.
VLANet: Video-Language Alignment Network for Weakly-Supervised Video Moment Retrieval
This paper explores methods for performing VMR in a weakly-supervised manner (wVMR): training is performed without temporal moment labels but only with the text query that describes a segment of the video.
Chip Placement with Deep Reinforcement Learning
To achieve these results, we pose placement as a Reinforcement Learning (RL) problem and train an agent to place the nodes of a chip netlist onto a chip canvas.
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