Self-Monitoring Navigation Agent via Auxiliary Progress Estimation
Table 3: Performance comparison with the state of arts without beam search: Student-forcing (Anderson et al., 2018b), RPA (Wang et al., 2018b), and Speaker-Follower (Fried et al., 2018). *: with data augmentation.
['[EMPTY]', 'Validation-Seen', 'Validation-Seen', 'Validation-Seen', 'Validation-Seen', 'Validation-Unseen', 'Validation-Unseen', 'Validation-Unseen', 'Validation-Unseen', 'Test (unseen)', 'Test (unseen)', 'Test (unseen)', 'Test (unseen)']
We provide the comparison with state of the arts without using beam search. We can see that our proposed method outperformed existing approaches with a large margin on both validation unseen and test sets. Our method with greedy decoding for action selection improved the SR by 9% and 8% on validation unseen and test set. When using progress inference for action selection, the performance on the test set significantly improved by 5% compared to using greedy decoding, yielding 13% improvement over the best existing approach.