Generating Test Input with Deep Reinforcement Learning 论文笔记
论文地址 摘要 Searchbased Software Testing (SBST)使用metaheuristic algorithms（元启发式算法）自动生成测试数据。元启发式算法是基于 fitness function返回值不断试错的方法，这与强化学习的过程很相似。本文研究在SBST中用rf的方法代替人工设计的元启发式算法，我们把被测软件（SUT）改写为强化学习环境。同时，我们提出了一种新型
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相关文章
- 1. Generating Text with Deep Reinforcement Learning
- 2. 论文笔记：《Playing Atari with Deep Reinforcement Learning》
- 3. 论文笔记《Neural Architecture Search With Reinforcement Learning》
- 4. 【论文翻译】Playing Atari with Deep Reinforcement Learning
- 5. 论文笔记——NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
- 6. 【论文笔记】NEURAL COMBINATORIAL OPTIMIZATION WITH REINFORCEMENT LEARNING
- 7. 论文笔记 Reinforcement Learning with Derivative-Free Exploration
- 8. Mastering Complex Control in MOBA Games with Deep Reinforcement Learning（论文笔记）
- 9. Hierarchical Object Detection with Deep Reinforcement Learning论文笔记
- 10. 论文阅读笔记（五十二）：Outline Objects using Deep Reinforcement Learning