BabyWalk: Going Farther in Vision-and-Language Navigationby Taking Baby Steps
Table 3: The memory buffer is beneficial to generalizing to different tasks from on which the agent is trained.
['Setting Metrics', 'r4r → r4r sr↑', 'r4r → r4r cls↑', 'r4r → r4r sdtw ↑', 'r4r → others sr↑', 'r4r → others cls↑', 'r4r → others sdtw ↑']
Without the memory (null), generalization to longer tasks is significantly worse. Using LSTM to summarize is worse than using forgetting to summarize (eqs. Meanwhile, ablating γ of the forgetting mechanism concludes that γ=0.5 is the optimal to our hyperparameter search. Note that when γ=0, this mechanism degenerates to taking average of the memory buffer, and leads to inferior results.