Contextual Bandits find important use cases in various real-life scenarios
such as online advertising, recommendation systems, healthcare, etc. However,
most of the algorithms use flat feature vectors to represent context whereas,
in the real world, there is a varying number of objects and relations among
them to model in the context. For example, in a music recommendation system,
the user context contains what music they listen to, which artists create this
music, the artist albums, etc. Adding richer relational context representations
also introduces a much larger context space making exploration-exploitation
harder. To improve the efficiency of exploration-exploitation knowledge about
the context can be infused to guide the exploration-exploitation strategy.
Relational context representations allow a natural way for humans to specify
knowledge owing to their descriptive nature. We propose an adaptation of
Knowledge Infused Policy Gradients to the Contextual Bandit setting and a novel
Knowledge Infused Policy Gradients Upper Confidence Bound algorithm and perform
an experimental analysis of a simulated music recommendation dataset and
various real-life datasets where expert knowledge can drastically reduce the
total regret and where it cannot.

本文提出了一种将知识注入到策略梯度上限边界算法中的上下文策略选择方法，并在音乐推荐数据集和各种现实生活数据集上进行了实验分析，结果表明专家知识能够极大地减少总遗憾。