Deep reinforcement learning for conversational robots playing games

@article{Cuayx00E1huitl2017DeepRL,
  title={Deep reinforcement learning for conversational robots playing games},
  author={Heriberto Cuayx00E1huitl},
  journal={2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)},
  year={2017},
  pages={771-776}
}
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… CONTINUE READING