Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied Agents
Table 5: The evaluation results for 10k test sessions on 10×10 maps. The average navigation success rates are reported as percentages. The second number in each cell represents the standard deviation over twelve test runs, each run corresponding to one pass (out of three final passes) of one trained model (out of four random initializations).
['[EMPTY]', 'nav 2D', 'nav 3D', 'nav_avoid 2D', 'nav_avoid 3D', 'nav_bw 2D', 'nav_bw 3D', 'nav_dir 2D', 'nav_dir 3D', 'nav_near 2D', 'nav_near 3D']
Y=10, with 6 goals and 24 obstacles, and X=Y=11, with 8 goals and 28 obstacles. Although the performance is not as good as on 8×8 maps (except for nav_avoid whose chance performance tends to peak as there are more goals on the map before the map size becoming too large), the GFT agents achieve reasonable generalizations and still greatly outperform the comparison methods. This further demonstrates that our GFT agents are not trained to memorize environments in specific settings.