Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games
This work addresses the lack of reasoning capabilities in RL agents for text-based games, which is an incremental improvement for the domain of interactive natural language simulations.
The paper tackles the problem of enabling explicit reasoning in reinforcement learning agents for text-based games by using knowledge graphs and a stacked hierarchical attention mechanism, resulting in improved performance over existing agents on benchmark games.
We study reinforcement learning (RL) for text-based games, which are interactive simulations in the context of natural language. While different methods have been developed to represent the environment information and language actions, existing RL agents are not empowered with any reasoning capabilities to deal with textual games. In this work, we aim to conduct explicit reasoning with knowledge graphs for decision making, so that the actions of an agent are generated and supported by an interpretable inference procedure. We propose a stacked hierarchical attention mechanism to construct an explicit representation of the reasoning process by exploiting the structure of the knowledge graph. We extensively evaluate our method on a number of man-made benchmark games, and the experimental results demonstrate that our method performs better than existing text-based agents.