Papers with TextWorld framework
Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning (N19-1)
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| Challenge: | Text adventure games provide a platform for exploring reinforcement learning in combinatorial action space, such as natural language. |
| Approach: | They propose a deep reinforcement learning architecture that represents the game state as a knowledge graph which is learned during exploration. |
| Outcome: | The proposed architecture can learn a control policy faster than baseline alternatives. |