Baselines for Reinforcement Learning in Text Games
  The ability to learn optimal control policies in systems where action space
is defined by sentences in natural language would allow many interesting
real-world applications such as automatic optimisation of dialogue systems.
Text-based games with multiple endings and rewards are a promising platform for
this task, since their feedback allows us to employ reinforcement learning
techniques to jointly learn text representations and control policies. We argue
that the key property of AI agents, especially in the text-games context, is
their ability to generalise to previously unseen games. We present a
minimalistic text-game playing agent, testing its generalisation and transfer
learning performance and showing its ability to play multiple games at once. We
also present pyfiction, an open-source library for universal access to
different text games that could, together with our agent that implements its
interface, serve as a baseline for future research.
