Novice programmers often struggle with the formal syntax of programming
languages. To assist them, we design a novel programming language correction
framework amenable to reinforcement learning. The framework allows an agent to
mimic human actions for text navigation and editing. We demonstrate that the
agent can be trained through self-exploration directly from the raw input, that
is, program text itself, without any knowledge of the formal syntax of the
programming language. We leverage expert demonstrations for one tenth of the
training data to accelerate training. The proposed technique is evaluated on
6975 erroneous C programs with typographic errors, written by students during
an introductory programming course. Our technique fixes 14% more programs and
29% more compiler error messages relative to those fixed by a state-of-the-art
tool, DeepFix, which uses a fully supervised neural machine translation
approach.

针对初学者编程者在编程语言上的困惑，通过一种新颖的基于强化学习的编程语言纠错框架，利用专家演示以及自我探索等方式进行训练提升，使得其对有错误的程序进行更好的修复，相对于最先进的工具 DeepFix 在修复错误和更多的编译器错误信息上提高了 14% 和 29%