  To solve a text-based game, an agent needs to formulate valid text commands
for a given context and find the ones that lead 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. The best model can
generate valid commands which were unseen at training and achieve high $F_1$
score on the test set.
