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arXiv:1709.05638 (cs)
[Submitted on 17 Sep 2017 (v1), last revised 19 Aug 2018 (this version, v2)]

Title:Improving Search through A3C Reinforcement Learning based Conversational Agent

Authors:Milan Aggarwal, Aarushi Arora, Shagun Sodhani, Balaji Krishnamurthy
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Abstract:We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent. Our approach caters to subjective search where the user is seeking digital assets such as images which is fundamentally different from the tasks which have objective and limited search modalities. Labeled conversational data is generally not available in such search tasks and training the agent through human interactions can be time consuming. We propose a stochastic virtual user which impersonates a real user and can be used to sample user behavior efficiently to train the agent which accelerates the bootstrapping of the agent. We develop A3C algorithm based context preserving architecture which enables the agent to provide contextual assistance to the user. We compare the A3C agent with Q-learning and evaluate its performance on average rewards and state values it obtains with the virtual user in validation episodes. Our experiments show that the agent learns to achieve higher rewards and better states.
Comments: 17 pages, 7 figures
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:1709.05638 [cs.AI]
  (or arXiv:1709.05638v2 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.1709.05638
arXiv-issued DOI via DataCite

Submission history

From: Milan Aggarwal [view email]
[v1] Sun, 17 Sep 2017 10:56:41 UTC (1,460 KB)
[v2] Sun, 19 Aug 2018 08:00:34 UTC (3,267 KB)
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Milan Aggarwal
Aarushi Arora
Shagun Sodhani
Balaji Krishnamurthy
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