Working Memory Graphs
Table 1: BabyAI sample efficiency: the amount of training (shown here in thousands of environment interactions) required for a model to solve 99% of 10,000 episodes. Hyperparameters were first tuned on each model/format/level combination separately, then each reported result was computed as the median sample efficiency over 100 additional training runs. Dashes indicate that no model reliably reached a solution rate of 99% within 6 million training steps (environment interactions). Note that Chevalier-Boisvert et al. (2018) report sample efficiencies in terms of episodes rather than environment interactions. (See Table 15.)
['Model Image format', 'WMG factored', 'nr-WMG factored', 'GRU factored', 'WMG flat', 'GRU flat', 'CNN+GRU native 7x7x3']
Notably, WMG with factored images achieved sample efficiencies 10x greater (on Level 3) than CNN+GRU using the native 7x7 image format. However, factored observations alone are not sufficient for sample efficiency: WMG utilized factored images much more effectively than a GRU on Levels 2-5. This result supports our hypothesis that Transformer-based models are particularly well suited for operating on set-based inputs like factored observations, and large gains in sample efficiency are observed as a result.