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Learning to Win by Reading Manuals in a Monte Carlo Framework

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Academic year: 2020

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Figure 2: The structure of our model.Each rectan-active unitsgle represents a collection of units in a layer, and theshaded trapezoids show the connections between layers.A fixed, real-valued feature function ⃗x ( s , a , d ) transformsthe game state s , ac
Figure 3: Example attributes of the game (box above),and features computed using the game manual and theseattributes (box below).
Table 1: Win rate of our method and several baselineswithin the first 100 game steps, while playing against thebuilt-in game AI
Figure 4: Examples of our method’s sentence relevanceand predicate labeling decisions
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