Attention Based Natural Language Grounding by Navigating Virtual Environment
Table 2: Comparison of our model with other baseline methods on unseen scenario generalization for 2D environment
['Model', 'mean reward', 'success rate']
The success rate and mean reward values are averaged over 100 episodes. We observe from the table that n=5 achieves best mean reward of 0.95. We also found that when A3Cattn (n=5) is evaluated under zero-shot generalization settings, it obtains a mean reward of 0.8. For zero-shot evaluation, we measure the agent’s success rate on 19 instructions that were held out during training and these instructions consist of new combinations of attribute-object pairs not seen before by the agent during train phase (refer supplementary section for these instructions).