Online Reinforcement Learning from Concurrent Customer Interaction Sequences

author: David Silver, Department of Computer Science, University College London
published: May 28, 2013,   recorded: September 2012,   views: 4171

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This talk explores applications in which a company interacts 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 ofthis setting is that interactions with different customers occur asynchronously and 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. I will present the first framework for reinforcement learning in this setting, using an asynchronous variant of temporal-difference learning to learn efficiently from partial interaction sequences.

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Reviews and comments:

Comment1 Lars Rigter, November 10, 2017 at 10:39 a.m.:

Dear Mr Silver,
I have a question about the concurrent reinforcement learning algorithm.
What if one customer likes to receive a lot of newspapers and another customer really does not like to receive a newspaper at all. Will the algorithm be able to customize it for both customers or will it concurrently learn from the first customer to sent a lot of emails to the other customer as well.

Thank you in advance.

Best Regards,

Lars Rigter

Comment2 Carl Marx, October 15, 2018 at 8:03 a.m.:

It is challenging for the customers and their re-enforcement and anyone can convert them as per customers requests.

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