The test cases are obtained with the assistance of a customized fuzzer and are only required during pre-training.
The effectiveness of the proposed method is verified on two program understanding tasks including code clone detection and code classification, and it outperforms current state-of-the-arts by large margins.
Deep reinforcement learning (DRL) for resource allocation has been investigated extensively owing to its ability of handling model-free and end-to-end problems.
Deep reinforcement learning has been applied for a variety of wireless tasks, which is however known with high training and inference complexity.
Predictive power allocation is conceived for energy-efficient video streaming over mobile networks using deep reinforcement learning.
This paper focuses on learning both local semantic and global structure representations for text classification.