Formatting Instructions For NeurIPS 2020
Table 1: System performance in the single-domain setting on 2000 dialogues in the target domains.
['[BOLD] Systems', 'Hotel  [BOLD] Success', 'Hotel  [BOLD] Reward', 'Hotel  [BOLD] Turns', 'Train  [BOLD] Success', 'Train  [BOLD] Reward', 'Train  [BOLD] Turns', 'Police  [BOLD] Success', 'Police  [BOLD] Reward', 'Police  [BOLD] Turns', 'Average  [BOLD] Success', 'Average  [BOLD] Reward', 'Average  [BOLD] Turns']
Dialogue tasks in police are relatively easy to accomplish, on which DQN-1k trained from scratch with only 1,000 frames can completely succeed. On the contrary, DQN-1k fails on all the tasks in Hotel. Also, note that Dtqn-1k significantly outperforms Dqn-1k across all domains. This demonstrates the effectiveness of modeling the dependency between the state and action space. Besides, the performance gain from meta-training is more significant in the train domain (i.e., 4.8% in success rate), which can be attributed to the similarity of state and action spaces between train and the source domain taxi.