Deep reinforcement learning for conversational robots playing games

Cuayahuitl, Heriberto (2017) Deep reinforcement learning for conversational robots playing games. In: IEEE RAS International Conference on Humanoid Robots, 15 - 17 November 2017, REP Theatre, Birmingham, UK.

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Item Type:Conference or Workshop contribution (Presentation)
Item Status:Live Archive


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 algorithms in agents playing the game of Noughts and Crosses with two grid sizes (3x3 and 5x5). Experimental results show evidence that our proposed method can lead to more effective policies than the baseline DQN method, which can be used for training interactive social robots.

Keywords:Deep Reinforcement Learning, Interactive Robots
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G710 Speech and Natural Language Processing
H Engineering > H671 Robotics
G Mathematical and Computer Sciences > G730 Neural Computing
Divisions:College of Science > School of Computer Science
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ID Code:29060
Deposited On:12 Oct 2017 12:28

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