Learn How to Cook a New Recipe in a New House: Using Map Familiarization, Curriculum Learning, and Bandit Feedback to Learn Families of Text-Based Adventure Games
Table 4: Breakdown of information conversion strategies of tier-4 to tier-6 on both Test 1 and Test 2 with the best model after curriculumn learning on tier-6, without the final fine-tuning; ‘go-cardinal’ is the worst since the instance-knowledge learning is overfit on each single game. The ‘go-random’ approach is less effective as map size increases, while the ‘go-room’ method performs the best for both test sets.
['Test', 'Tier', 'go-cardinal', 'go-random', 'go-room']
The model used for evaluation is the best model on the dev set after the tier-6 training of curriculum learning, without the final fine-tune. It is clear that ‘go-cardinal,’ which does not convert any instance information, is less able to learn than the other methods at any tier. As the number of rooms to navigate grows from tier-4 to tier-6, the random navigation strategy becomes less effective, such that the ‘go-room’ transferring from instance-level cardinal information into universal-level room transition information is the most effective at navigating the large twelve-room games of tier-6.