2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids) 2017
DOI: 10.1109/humanoids.2017.8246959
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Deep reinforcement learning for conversational robots playing games

Abstract: Deep reinforcement learning for interactive multimodal robots is attractive for endowing machines with trainable skill acquisition. But this form of learning still represents several challenges. The challenge that we focus in this paper is effective policy learning. To address that, in this paper we compare the Deep Q-Networks (DQN) method against a variant that aims for stronger decisions than the original method by avoiding decisions with the lowest negative rewards. We evaluated our baseline and proposed al… Show more

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Cited by 7 publications
(1 citation statement)
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“…The application of artificial conversational agents to communicate information on behalf of humans has been a major trend that is being utilized intensively, especially in the field of commerce [1], [2], [3], [4], [5], entertainment [6], tourism [7], medical [8], [9], [10], [11], [12], [13] as well as education [14], [15], [16], [17], [18]. According to the author in [19], by implementing AI, the banking industry could enjoy possible cost savings of up to 25 percent in Information Technology operations.…”
Section: Introductionmentioning
confidence: 99%
“…The application of artificial conversational agents to communicate information on behalf of humans has been a major trend that is being utilized intensively, especially in the field of commerce [1], [2], [3], [4], [5], entertainment [6], tourism [7], medical [8], [9], [10], [11], [12], [13] as well as education [14], [15], [16], [17], [18]. According to the author in [19], by implementing AI, the banking industry could enjoy possible cost savings of up to 25 percent in Information Technology operations.…”
Section: Introductionmentioning
confidence: 99%