43 citations · 128 across the 28 of their papers we have counts for
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cs.LG2019★ 3 cited
Attentional Policies for Cross-Context Multi-Agent Reinforcement Learning
Matthew A. Wright, Roberto Horowitz
Many potential applications of reinforcement learning in the real world involve interacting with other agents whose numbers vary over time. We propose new neural policy architectur…
cs.LG2019★ 8 cited
Neural-Attention-Based Deep Learning Architectures for Modeling Traffic Dynamics on Lane Graphs
Matthew A. Wright, Simon F. G. Ehlers, Roberto Horowitz
Deep neural networks can be powerful tools, but require careful application-specific design to ensure that the most informative relationships in the data are learnable. In this pap…