Concurrent Reinforcement Learning from Customer Interactions


David Silver, Leonard Newnham, David Barker, Suzanne Weller, Jason McFall ;
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):924-932, 2013.


In this paper, we explore applications in which a company interacts concurrently with many customers. The company has an objective function, such as maximising revenue, customer satisfaction, or customer loyalty, which depends primarily on the sequence of interactions between company and customer. A key aspect of this setting is that interactions with different customers occur in parallel. As a result, it is imperative to learn online from partial interaction sequences, so that information acquired from one customer is efficiently assimilated and applied in subsequent interactions with other customers. We present the first framework for concurrent reinforcement learning, using a variant of temporal-difference learning to learn efficiently from partial interaction sequences. We evaluate our algorithms in two large-scale test-beds for online and email interaction respectively, generated from a database of 300,000 customer records.

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