  While Reinforcement Learning (RL) approaches lead to significant achievements
in a variety of areas in recent history, natural language tasks remained mostly
unaffected, due to the compositional and combinatorial nature that makes them
notoriously hard to optimize. With the emerging field of Text-Based Games
(TBGs), researchers try to bridge this gap. Inspired by the success of RL
algorithms on Atari games, the idea is to develop new methods in a restricted
game world and then gradually move to more complex environments. Previous work
in the area of TBGs has mainly focused on solving individual games. We,
however, consider the task of designing an agent that not just succeeds in a
single game, but performs well across a whole family of games, sharing the same
theme. In this work, we present our deep RL agent--LeDeepChef--that shows
generalization capabilities to never-before-seen games of the same family with
different environments and task descriptions. The agent participated in
Microsoft Research's "First TextWorld Problems: A Language and Reinforcement
Learning Challenge" and outperformed all but one competitor on the final test
set. The games from the challenge all share the same theme, namely cooking in a
modern house environment, but differ significantly in the arrangement of the
rooms, the presented objects, and the specific goal (recipe to cook). To build
an agent that achieves high scores across a whole family of games, we use an
actor-critic framework and prune the action-space by using ideas from
hierarchical reinforcement learning and a specialized module trained on a
recipe database.
