The core operation of current Graph Neural Networks (GNNs) is the aggreg...
Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using...
In reinforcement learning, the graph Laplacian has proved to be a valuab...
Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using...
We present an end-to-end, model-based deep reinforcement learning agent ...
The core operation of Graph Neural Networks (GNNs) is the aggregation en...
The performance limit of Graph Convolutional Networks (GCNs) and the fac...
Temporal-Difference (TD) learning is a standard and very successful
rein...
Image captioning is a research hotspot where encoder-decoder models comb...
Recently, neural network based approaches have achieved significant
impr...
Learning speed and accuracy are of universal interest for reinforcement
...
The infeasible parts of the objective space in difficult many-objective
...
Out of practical concerns and with the expectation to achieve high overa...
Researches have shown difficulties in obtaining proximity while maintain...