  This work presents an exploration and imitation-learning-based agent capable
of state-of-the-art performance in playing text-based computer games.
Text-based computer games describe their world to the player through natural
language and expect the player to interact with the game using text. These
games are of interest as they can be seen as a testbed for language
understanding, problem-solving, and language generation by artificial agents.
Moreover, they provide a learning environment in which these skills can be
acquired through interactions with an environment rather than using fixed
corpora. One aspect that makes these games particularly challenging for
learning agents is the combinatorially large action space. Existing methods for
solving text-based games are limited to games that are either very simple or
have an action space restricted to a predetermined set of admissible actions.
In this work, we propose to use the exploration approach of Go-Explore for
solving text-based games. More specifically, in an initial exploration phase,
we first extract trajectories with high rewards, after which we train a policy
to solve the game by imitating these trajectories. Our experiments show that
this approach outperforms existing solutions in solving text-based games, and
it is more sample efficient in terms of the number of interactions with the
environment. Moreover, we show that the learned policy can generalize better
than existing solutions to unseen games without using any restriction on the
action space.
