论文标题
TSETLIN机器用于解决上下文匪徒问题
Tsetlin Machine for Solving Contextual Bandit Problems
论文作者
论文摘要
本文使用TSETLIN机器介绍了可解释的上下文Bandit算法，该机器使用命题逻辑解决了复杂的模式识别任务。提出的匪徒学习算法依赖于直接的位操纵，从而简化了计算和解释。然后，鉴于其非参数性质，我们提出了一种用Tsetlin机器进行汤普森采样的机制。我们的经验分析表明，Tsetlin机器作为基本上下文的强盗学习者在九个数据集中的八个中优于其他流行的基础学习者。我们进一步分析了学习者的解释性，并根据对环境的命题表达式进行了研究。
This paper introduces an interpretable contextual bandit algorithm using Tsetlin Machines, which solves complex pattern recognition tasks using propositional logic. The proposed bandit learning algorithm relies on straightforward bit manipulation, thus simplifying computation and interpretation. We then present a mechanism for performing Thompson sampling with Tsetlin Machine, given its non-parametric nature. Our empirical analysis shows that Tsetlin Machine as a base contextual bandit learner outperforms other popular base learners on eight out of nine datasets. We further analyze the interpretability of our learner, investigating how arms are selected based on propositional expressions that model the context.
