Representation Learning for Grounded Spatial Reasoning
Table 3: Performance on a test set of environments and instructions after supervised training. Lower is better for MSE and Manhattan distance; higher is better for policy quality. The gradient basis significantly improves the reconstruction error and goal localization of our model on global instructions, and expectedly does not affect its performance on local instructions.
['[EMPTY]', 'Local MSE', 'Local Policy Quality', 'Local Distance', 'Global MSE', 'Global Policy Quality', 'Global Distance']
S6SS1SSS0Px2 Analysis of learned representations For the representation analysis in a supervised setting , we compared the predicted value maps of all models against the unseen test split of maps. As expected, our model without the global gradient performs no differently from the full model on local references, but has higher MSE and average distances to true goal than the full model on global references. We also note that UVFA (pos) performs much worse than both CNN+LSTM and our model, showing the difficulty of environment generalization even when the goals are observed directly. Surprisingly, our model trained via reinforcement learning has more precise goal location predictions (as measured via Manhattan distance) than when trained on true state values in a supervised manner. However, the MSE of the value predictions are much higher in the RL setting (e.g., 0.80 vs 0.25 for supervised on local instructions). This shows that despite the comparative stability of the supervised setting, minimization of value prediction error does not necessarily lend itself to the best policy or goal localization.