Towards Solving Text-based Games by Producing Adaptive Action Spaces

To solve a text-based game, an agent needs to formulate valid text commands for a given context and find the one that leads to success. Recent attempts at solving text-based games with deep reinforcement learning have focused on the latter, i.e., learning to act optimally when valid actions are known in advance. In this work, we propose to tackle the first task and train a model that generates the set of all valid commands for a given context. We try three generative models on a dataset generated with Textworld (Côté et al., 2018). The best model can generate valid commands which were unseen at training and achieve high F1 score on the test set.