Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning

Understand

Text-based adventure games provide a platform on which to explore reinforcement learning in the context of a combinatorial action space, such as natural language.

• We present a deep reinforcement learning architecture that represents the game state as a knowledge graph which is learned during exploration.
• This graph is used to prune the action space, enabling more efficient exploration.
• The question of which action to take can be reduced to a question-answering task, a form of transfer learning that pre-trains certain parts of our architecture.