论文标题
通过增强学习与多头自动机的语言推断
Language Inference with Multi-head Automata through Reinforcement Learning
论文作者
论文摘要
本文的目的是使用强化学习来对可以识别形式语言的学习剂进行建模。代理被建模为简单的多头自动机，这是一种使用多个头部的有限自动机的新型号，六种不同的语言被表达为增强学习问题。两种不同的算法用于优化。第一个算法是Q学习，它训练门控复发单元以学习最佳策略。第二种是遗传算法，它通过使用进化启发的操作来搜索最佳解决方案。结果表明，遗传算法通常比Q学习算法更好，但Q学习算法可以更快地找到常规语言的解决方案。
The purpose of this paper is to use reinforcement learning to model learning agents which can recognize formal languages. Agents are modeled as simple multi-head automaton, a new model of finite automaton that uses multiple heads, and six different languages are formulated as reinforcement learning problems. Two different algorithms are used for optimization. First algorithm is Q-learning which trains gated recurrent units to learn optimal policies. The second one is genetic algorithm which searches for the optimal solution by using evolution inspired operations. The results show that genetic algorithm performs better than Q-learning algorithm in general but Q-learning algorithm finds solutions faster for regular languages.
