Our formulation uses a novel Fourier object disentanglement method to innately separate out the human agent (which is typically small) from the background.
We present a new traffic dataset, METEOR, which captures traffic patterns and multi-agent driving behaviors in unstructured scenarios.
cost), by integrating R-learning, a tabular reinforcement learning (RL) algorithm tailored for maximizing the long-term average reward, and traditional DRL algorithms, initially developed to optimize the discounted long-term cumulative reward rather than the average one.
More gracefully, our DRConv transfers the increasing channel-wise filters to spatial dimension with learnable instructor, which not only improve representation ability of convolution, but also maintains computational cost and the translation-invariance as standard convolution dose.
Benefitted from its great success on many tasks, deep learning is increasingly used on low-computational-cost devices, e. g. smartphone, embedded devices, etc.