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SUPPLEMENTARY MATERIAL Deep Reinforcement Learning with Stacked Hierarchical Attention for T based Games
Figure 1 shows an example of the raw interface of the game "ztuu", where raw textual observations In this section, we show the first 15 interaction steps of two games: "zork1" and "ztuu". C h o s e n a c t i o n a n d r e w a r d A c t i o n: w e s t Reward: 0 | S c o r e: 0 ===== S t e p 2 ===== ===== 1 . C h o s e n a c t i o n a n d r e w a r d A c t i o n: s o u t h Reward: 0 | S c o r e: 0 ===== S t e p 3 ===== 16 ===== 1 . C h o s e n a c t i o n a n d r e w a r d A c t i o n: s o u t h Reward: 0 | S c o r e: 0 ===== S t e p 4 ===== ===== 1 . C h o s e n a c t i o n a n d r e w a r d A c t i o n: w e s t Reward: 0 | S c o r e: 0 ===== S t e p 5 ===== ===== 1 .
SUPPLEMENTARY MATERIAL Deep Reinforcement Learning with Stacked Hierarchical Attention for T based Games
Figure 1 shows an example of the raw interface of the game "ztuu", where raw textual observations In this section, we show the first 15 interaction steps of two games: "zork1" and "ztuu". C h o s e n a c t i o n a n d r e w a r d A c t i o n: w e s t Reward: 0 | S c o r e: 0 ===== S t e p 2 ===== ===== 1 . C h o s e n a c t i o n a n d r e w a r d A c t i o n: s o u t h Reward: 0 | S c o r e: 0 ===== S t e p 3 ===== 16 ===== 1 . C h o s e n a c t i o n a n d r e w a r d A c t i o n: s o u t h Reward: 0 | S c o r e: 0 ===== S t e p 4 ===== ===== 1 . C h o s e n a c t i o n a n d r e w a r d A c t i o n: w e s t Reward: 0 | S c o r e: 0 ===== S t e p 5 ===== ===== 1 .