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Contextual Bandits find important use cases in various real-life scenariossuch 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 amongthem to model in the context. For example, in a music recommendation system,the user context contains what music they listen to, which artists create thismusic, the artist albums, etc. Adding richer relational context representationsalso introduces a much larger context space making exploration-exploitationharder. To improve the efficiency of exploration-exploitation knowledge aboutthe context can be infused to guide the exploration-exploitation strategy.Relational context representations allow a natural way for humans to specifyknowledge owing to their descriptive nature. We propose an adaptation ofKnowledge Infused Policy Gradients to the Contextual Bandit setting and a novelKnowledge Infused Policy Gradients Upper Confidence Bound algorithm and performan experimental analysis of a simulated music recommendation dataset andvarious real-life datasets where expert knowledge can drastically reduce thetotal regret and where it cannot.

Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits