  Text-based games present a unique class of sequential decision making problem
in which agents interact with a partially observable, simulated environment via
actions and observations conveyed through natural language. Such observations
typically include instructions that, in a reinforcement learning (RL) setting,
can directly or indirectly guide a player towards completing reward-worthy
tasks. In this work, we study the ability of RL agents to follow such
instructions. We conduct experiments that show that the performance of
state-of-the-art text-based game agents is largely unaffected by the presence
or absence of such instructions, and that these agents are typically unable to
execute tasks to completion. To further study and address the task of
instruction following, we equip RL agents with an internal structured
representation of natural language instructions in the form of Linear Temporal
Logic (LTL), a formal language that is increasingly used for temporally
extended reward specification in RL. Our framework both supports and highlights
the benefit of understanding the temporal semantics of instructions and in
measuring progress towards achievement of such a temporally extended behaviour.
Experiments with 500+ games in TextWorld demonstrate the superior performance
of our approach.
